{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "TEYk3YhjyOjh" }, "source": [ "# 7. Reinforcement learning for Control 🐶\n", "\n", "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "3QrXLWk3Ay_2" }, "source": [ "```{attention} \n", "In this tutorial we are going to use the same CSTR example as in [tutorial notebook 6](https://edgarsmdn.github.io/MLCE_book/06_PID_tuning.html). Therefore, it is a great idea to first look at tutorial 6 to have the complete context.\n", "```\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "fkCNfmupyGtP" }, "source": [ "## Goals of this exercise 🌟\n", "- Perform reactor control using reinforcement learning\n", "- Revise the concept of transfer learning\n", "- Revise the concept of policy gradients\n" ] }, { "cell_type": "markdown", "metadata": { "id": "qZQaAgygyipA" }, "source": [ "## A quick reminder ✅" ] }, { "cell_type": "markdown", "metadata": { "id": "x_1lVhKB6agf" }, "source": [ "Reinforcement Learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. \n", "\n", "RL algorithms are particularly well suited to address sequential decision making problems under uncertainty, for example, they are generally applied to solve problems in a Markov decision process (MDP) setting. A control problem (just like reactor control) is a sequential decision making problem under uncertainty, where at every time-step the controller (agent in the RL context) must take an optimal (control) action, and it is hindered by process disturbances (uncertainty). There are many types of reinforcement leanring algorithms, in this notebook tutorial we will focus on **policy optimization** algorithms. \n", "\n", "We can define an RL agent as a controller that given a state ${\\bf x}$ outputs the optimal action ${\\bf u}$\n", "\n", "$${\\bf u}:=\\pi({\\bf x})$$\n", "\n", "If the controller $\\pi(\\cdot)$ is parametrized, say by neural network weights $\\boldsymbol{\\theta}$, we can write \n", "\n", "$${\\bf u}:=\\pi({\\bf x};\\boldsymbol{\\theta})$$\n", "\n", "where ${\\bf \\theta}$ are parameters determined *a priori*. We could define the PID controller in this same fashion:\n", "\n", "$$u:=\\text{PID}(x;K_P,K_I,K_D)$$\n", "\n", "In many cases, to fullfil the exploration - exploitation dilemma or in games, stochastic policies are used, which instead of outputting a single action ${\\bf u}$, output a distributions over actions $p({\\bf x};\\boldsymbol{\\theta})$. \n", "\n", "$${\\bf u} \\sim p({\\bf x};\\boldsymbol{\\theta})$$\n", "\n", "In practice, it is common to have a neural network output the moments (mean and variance), and to then draw an action from the distribution parametrized by this mean and variance\n", "\n", "$$ \\boldsymbol{ \\mu }, \\boldsymbol{ \\Sigma } := p({\\bf x};\\boldsymbol{\\theta})$$\n", "\n", "$${\\bf u} \\sim \\mathcal{N}(\\boldsymbol{ \\mu }, \\boldsymbol{ \\Sigma })$$\n", "\n", "You can find a Taxonomy of RL Algorithms below.\n", "\n", "```{figure} media/07_RL/algorithms.PNG\n", ":alt: kNN\n", ":width: 100%\n", ":align: center\n", "\n", "A broad classification of reinforcement learning algorithms. [source](https://spinningup.openai.com/en/latest/spinningup/rl_intro2.html)\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "go948-hx4dpr" }, "outputs": [], "source": [ "import torch\n", "import torch.nn.functional as Ffunctional\n", "import copy\n", "import numpy as np\n", "from scipy.integrate import odeint\n", "import matplotlib.pyplot as plt\n", "from pylab import grid\n", "import time" ] }, { "cell_type": "markdown", "metadata": { "id": "SP1vwwwCBns6" }, "source": [ "The code below corresponds to the CSTR model and parameters of tutorial 6." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "H1kEoxO3BvWb" }, "outputs": [], "source": [ "#@title CSTR code from tutorial 6\n", "\n", "eps = np.finfo(float).eps\n", "\n", "###############\n", "# CSTR model #\n", "###############\n", "\n", "# Taken from http://apmonitor.com/do/index.php/Main/NonlinearControl\n", "\n", "def cstr(x,t,u):\n", "\n", " # == Inputs == #\n", " Tc = u # Temperature of cooling jacket (K)\n", "\n", " # == States == #\n", " Ca = x[0] # Concentration of A in CSTR (mol/m^3)\n", " T = x[1] # Temperature in CSTR (K)\n", "\n", " # == Process parameters == #\n", " Tf = 350 # Feed temperature (K)\n", " q = 100 # Volumetric Flowrate (m^3/sec)\n", " Caf = 1 # Feed Concentration (mol/m^3)\n", " V = 100 # Volume of CSTR (m^3)\n", " rho = 1000 # Density of A-B Mixture (kg/m^3)\n", " Cp = 0.239 # Heat capacity of A-B Mixture (J/kg-K)\n", " mdelH = 5e4 # Heat of reaction for A->B (J/mol)\n", " EoverR = 8750 # E -Activation energy (J/mol), R -Constant = 8.31451 J/mol-K\n", " k0 = 7.2e10 # Pre-exponential factor (1/sec)\n", " UA = 5e4 # U -Heat Transfer Coefficient (W/m^2-K) A -Area - (m^2)\n", " \n", " # == Equations == #\n", " rA = k0*np.exp(-EoverR/T)*Ca # reaction rate\n", " dCadt = q/V*(Caf - Ca) - rA # Calculate concentration derivative\n", " dTdt = q/V*(Tf - T) \\\n", " + mdelH/(rho*Cp)*rA \\\n", " + UA/V/rho/Cp*(Tc-T) # Calculate temperature derivative\n", "\n", " # == Return xdot == #\n", " xdot = np.zeros(2)\n", " xdot[0] = dCadt\n", " xdot[1] = dTdt\n", " return xdot\n", "\n", "data_res = {} \n", "# Initial conditions for the states\n", "x0 = np.zeros(2)\n", "x0[0] = 0.87725294608097\n", "x0[1] = 324.475443431599\n", "data_res['x0'] = x0\n", "\n", "# Time interval (min)\n", "n = 101 # number of intervals\n", "tp = 25 # process time (min)\n", "t = np.linspace(0,tp,n)\n", "data_res['t'] = t\n", "data_res['n'] = n\n", "\n", "# Store results for plotting\n", "Ca = np.zeros(len(t)); Ca[0] = x0[0]\n", "T = np.zeros(len(t)); T[0] = x0[1] \n", "Tc = np.zeros(len(t)-1); \n", "\n", "data_res['Ca_dat'] = copy.deepcopy(Ca)\n", "data_res['T_dat'] = copy.deepcopy(T) \n", "data_res['Tc_dat'] = copy.deepcopy(Tc)\n", "\n", "# noise level\n", "noise = 0.1\n", "data_res['noise'] = noise\n", "\n", "# control upper and lower bounds\n", "data_res['Tc_ub'] = 305\n", "data_res['Tc_lb'] = 295\n", "Tc_ub = data_res['Tc_ub']\n", "Tc_lb = data_res['Tc_lb']\n", "\n", "# desired setpoints\n", "n_1 = int(n/2)\n", "n_2 = n - n_1\n", "Ca_des = [0.8 for i in range(n_1)] + [0.9 for i in range(n_2)]\n", "T_des = [330 for i in range(n_1)] + [320 for i in range(n_2)]\n", "data_res['Ca_des'] = Ca_des\n", "data_res['T_des'] = T_des\n", "\n", "##################\n", "# PID controller #\n", "##################\n", "\n", "def PID(Ks, x, x_setpoint, e_history):\n", "\n", " Ks = np.array(Ks)\n", " Ks = Ks.reshape(7, order='C')\n", "\n", " # K gains\n", " KpCa = Ks[0]; KiCa = Ks[1]; KdCa = Ks[2]\n", " KpT = Ks[3]; KiT = Ks[4]; KdT = Ks[5]; \n", " Kb = Ks[6]\n", " # setpoint error\n", " e = x_setpoint - x\n", " # control action\n", " u = KpCa*e[0] + KiCa*sum(e_history[:,0]) + KdCa*(e[0]-e_history[-1,0])\n", " u += KpT *e[1] + KiT *sum(e_history[:,1]) + KdT *(e[1]-e_history[-1,1])\n", " u += Kb\n", " u = min(max(u,data_res['Tc_lb']),data_res['Tc_ub'])\n", "\n", " return u" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "X6neta1WHgIW" }, "outputs": [], "source": [ "#@title Ploting routines\n", "\n", "####################################\n", "# plot control actions performance #\n", "####################################\n", "\n", "def plot_simulation(Ca_dat, T_dat, Tc_dat, data_simulation): \n", " \n", " Ca_des = data_simulation['Ca_des']\n", " T_des = data_simulation['T_des']\n", " \n", " plt.figure(figsize=(8, 5))\n", "\n", " plt.subplot(3,1,1)\n", " plt.plot(t, np.median(Ca_dat,axis=1), 'r-', lw=3)\n", " plt.gca().fill_between(t, np.min(Ca_dat,axis=1), np.max(Ca_dat,axis=1), \n", " color='r', alpha=0.2)\n", " plt.step(t, Ca_des, '--', lw=1.5, color='black')\n", " plt.ylabel('Ca (mol/m^3)')\n", " plt.xlabel('Time (min)')\n", " plt.legend(['Concentration of A in CSTR'],loc='best')\n", " plt.xlim(min(t), max(t))\n", "\n", " plt.subplot(3,1,2)\n", " plt.plot(t, np.median(T_dat,axis=1), 'c-', lw=3)\n", " plt.gca().fill_between(t, np.min(T_dat,axis=1), np.max(T_dat,axis=1), \n", " color='c', alpha=0.2)\n", " plt.step(t, T_des, '--', lw=1.5, color='black')\n", " plt.ylabel('T (K)')\n", " plt.xlabel('Time (min)')\n", " plt.legend(['Reactor Temperature'],loc='best')\n", " plt.xlim(min(t), max(t))\n", "\n", " plt.subplot(3,1,3)\n", " plt.step(t[1:], np.median(Tc_dat,axis=1), 'b--', lw=3)\n", " plt.ylabel('Cooling T (K)')\n", " plt.xlabel('Time (min)')\n", " plt.legend(['Jacket Temperature'],loc='best')\n", " plt.xlim(min(t), max(t))\n", "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "##################\n", "# Training plots #\n", "##################\n", "\n", "def plot_training(data_simulation, repetitions):\n", " t = data_simulation['t'] \n", " Ca_train = np.array(data_simulation['Ca_train'])\n", " T_train = np.array(data_simulation['T_train'])\n", " Tc_train = np.array(data_simulation['Tc_train'])\n", " Ca_des = data_simulation['Ca_des']\n", " T_des = data_simulation['T_des']\n", "\n", " c_ = [(repetitions - float(i))/repetitions for i in range(repetitions)]\n", "\n", " plt.figure(figsize=(8, 5))\n", "\n", " plt.subplot(3,1,1)\n", " for run_i in range(repetitions):\n", " plt.plot(t, Ca_train[run_i,:], 'r-', lw=1, alpha=c_[run_i])\n", " plt.step(t, Ca_des, '--', lw=1.5, color='black')\n", " plt.ylabel('Ca (mol/m^3)')\n", " plt.xlabel('Time (min)')\n", " plt.legend(['Concentration of A in CSTR'],loc='best')\n", " plt.title('Training plots')\n", " plt.ylim([.75, .95])\n", " plt.xlim(min(t), max(t))\n", " grid(True)\n", "\n", " plt.subplot(3,1,2)\n", " for run_i in range(repetitions):\n", " plt.plot(t, T_train[run_i,:], 'c-', lw=1, alpha=c_[run_i])\n", " plt.step(t, T_des, '--', lw=1.5, color='black')\n", " plt.ylabel('T (K)')\n", " plt.xlabel('Time (min)')\n", " plt.legend(['Reactor Temperature'],loc='best')\n", " plt.ylim([335, 317])\n", " plt.xlim(min(t), max(t))\n", " grid(True)\n", "\n", " plt.subplot(3,1,3)\n", " for run_i in range(repetitions):\n", " plt.step(t[1:], Tc_train[run_i,:], 'b--', lw=1, alpha=c_[run_i])\n", " plt.ylabel('Cooling T (K)')\n", " plt.xlabel('Time (min)')\n", " plt.legend(['Jacket Temperature'],loc='best')\n", " plt.xlim(min(t), max(t))\n", " grid(True)\n", " \n", " plt.tight_layout()\n", "\n", " plt.show()\n", "\n", "#####################\n", "# Convergence plots #\n", "#####################\n", "\n", "def plot_convergence(Xdata, best_Y, Objfunc=None):\n", " '''\n", " Plots to evaluate the convergence of standard Bayesian optimization algorithms\n", " '''\n", " ## if f values are not given\n", " f_best = 1e8\n", " if best_Y==None: \n", " best_Y = []\n", " for i_point in range(Xdata.shape[0]):\n", " f_point = Objfunc(Xdata[i_point,:], collect_training_data=False)\n", " if f_point < f_best:\n", " f_best = f_point \n", " best_Y.append(f_best)\n", " best_Y = np.array(best_Y)\n", "\n", " n = Xdata.shape[0]\n", " aux = (Xdata[1:n,:]-Xdata[0:n-1,:])**2\n", " distances = np.sqrt(aux.sum(axis=1))\n", "\n", " ## Distances between consecutive x's\n", " plt.figure(figsize=(9,3))\n", " plt.subplot(1, 2, 1)\n", " plt.plot(list(range(n-1)), distances, '-ro')\n", " plt.xlabel('Iteration')\n", " plt.ylabel('d(x[n], x[n-1])')\n", " plt.title('Distance between consecutive x\\'s')\n", " plt.xlim(0, n)\n", " grid(True)\n", "\n", " # Best objective value found over iterations\n", " plt.subplot(1, 2, 2)\n", " plt.plot(list(range(n)), best_Y,'-o')\n", " plt.title('Value of the best selected sample')\n", " plt.xlabel('Iteration')\n", " plt.ylabel('Best y')\n", " grid(True)\n", " plt.xlim(0, n)\n", " plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "FM3HEnKIlfr5" }, "source": [ "## Stochastic Policy Search 🎲" ] }, { "cell_type": "markdown", "metadata": { "id": "htaEEVqKo5qn" }, "source": [ "### Policy network" ] }, { "cell_type": "markdown", "metadata": { "id": "pce9cEqIyYb9" }, "source": [ "In the same way as we used data-driven optimization to tune the gains $K_P,K_I,K_D$ in the PID controllers (cf. [tutorial notebook 6](https://edgarsmdn.github.io/MLCE_book/06_PID_tuning.html)), we can use the same approach to tune (or train) the parameters (or weights) $\\boldsymbol{\\theta}$ of a neural network. \n", "\n", "There is research that suggest that evolutionary (or stochastic search in general) algorithms can be as good (or better in some contexts) than traditional techniques, for more details see [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://openai.com/research/evolution-strategies), the paper can be found [here](https://arxiv.org/abs/1703.03864).\n", "\n", "The difference here, with respect to the controller tuning that we perform on [tutorial notebook 6](https://edgarsmdn.github.io/MLCE_book/06_PID_tuning.html) is that the neural networks have many parameters and the number of iterations needed are relatively high, and therefore, model/surrogate-based data-driven optimization methods do not scale as well and might not be the best choice. Therefore, stochastic search optimization methods (e.g. genetic algorithms, particle swarm optimization) can be a good alternative. You can check some pedagogical implementations [here](https://edgarsmdn.github.io/projects/stochastic_optimization_algorithms/).\n", "\n", "In the following section we build a relatively simple neural network controller in PyTorch\n", "\n", "$${\\bf u}:=\\pi({\\bf x};\\boldsymbol{\\theta})$$\n", "\n", "and hard code a simple stochastic search algorithm (it is a combination of random search and local random search) to manipulate the weights $\\boldsymbol{\\theta}$, evaluate the performance of the current weight values, and iterate.\n", "\n", "**Neural Network Controller Training Algorithm**\n", "\n", "*Initialization*\n", "\n", "Collect $d$ initial datapoints $\\mathcal{D}=\\{(\\hat{f}^{(j)}=\\sum_{k=0}^{k=T_f} (e(k))^2,~ \\boldsymbol{\\theta}^{(j)}) \\}_{j=0}^{j=d}$ by simulating $x(k+1) = f(x(\\cdot),u(\\cdot))$ for different values of $\\boldsymbol{\\theta}^{(j)}$, set a small radious of search $r$\n", "\n", "*Main loop*\n", "\n", "1. *Repeat*\n", "2. $~~~~~~$ Choose best current known parameter value $\\boldsymbol{\\theta}^*$.\n", "3. $~~~~~~$ Sample $n_s$ values around $\\boldsymbol{\\theta}^*$, that are at most some distance $r$, $\\bar{\\boldsymbol{\\theta}}^{(0)},...,\\bar{\\boldsymbol{\\theta}}^{(n_s)}$\n", "3. $~~~~~~$ Simulate new values $ x(k+1) = f(x(k),u(\\bar{\\boldsymbol{\\theta}}^{(i)};x(k))), ~ k=0,...,T_f-1, i=0,...,n_s $\n", "4. $~~~~~~$ Compute $\\hat{f}^{(i)}=\\sum_{k=0}^{k=T_f} (e(k))^2, i=0,...,n_s$.\n", "5. $~~~~~~$ **if** $\\bar{\\boldsymbol{\\theta}}^{\\text{best}}$ is better than $\\boldsymbol{\\theta}^*$, then $ \\boldsymbol{\\theta}^* \\leftarrow \\bar{\\boldsymbol{\\theta}}^{\\text{best}}$, **else** $ r \\leftarrow r\\gamma$, where $ 0 < \\gamma <1 $ \n", "6. until stopping criterion is met.\n", "\n", "Remarks: \n", "* The initial collection of $d$ points is generally done by some space filling (e.g. [Latin Hypercube](https://en.wikipedia.org/wiki/Latin_hypercube_sampling), [Sobol Sequence](https://en.wikipedia.org/wiki/Sobol_sequence)) procedure." ] }, { "cell_type": "markdown", "metadata": { "id": "NqlLSMqo5Ozc" }, "source": [ "First, let's create a neural network in PyTorch that has two hidden layers, one being double the size of the input layer and the other double the size of the output layer. We will use the activation functions [LeakyReLU](https://pytorch.org/docs/stable/generated/torch.nn.LeakyReLU.html) and [ReLU6](https://pytorch.org/docs/stable/generated/torch.nn.ReLU6.html). If you want a more gentle introduction to neural nets in Pytorch, check the [tutorial notebook 4](https://edgarsmdn.github.io/MLCE_book/04_DNN_VLE.html)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "C5M5fqpU8fd3" }, "outputs": [], "source": [ "##################\n", "# Policy Network #\n", "##################\n", "\n", "class Net(torch.nn.Module):\n", " # in current form this is a linear function (wouldn't expect great performance here)\n", " def __init__(self, **kwargs):\n", " super(Net, self).__init__()\n", "\n", " self.dtype = torch.float\n", "\n", " # Unpack the dictionary \n", " self.args = kwargs\n", "\n", " # Get info of machine\n", " self.use_cuda = torch.cuda.is_available() \n", " self.device = torch.device(\"cpu\")\n", "\n", " # Define ANN topology \n", " self.input_size = self.args['input_size']\n", " self.output_sz = self.args['output_size']\n", " self.hs1 = self.input_size*2\n", " self.hs2 = self.output_sz*2 \n", "\n", " # Define layers \n", " self.hidden1 = torch.nn.Linear(self.input_size, self.hs1 )\n", " self.hidden2 = torch.nn.Linear(self.hs1, self.hs2)\n", " self.output = torch.nn.Linear(self.hs2, self.output_sz)\n", "\n", " def forward(self, x):\n", " #x = torch.tensor(x.view(1,1,-1)).float() # re-shape tensor\n", " x = x.view(1, 1, -1).float()\n", " y = Ffunctional.leaky_relu(self.hidden1(x), 0.1)\n", " y = Ffunctional.leaky_relu(self.hidden2(y), 0.1)\n", " y = Ffunctional.relu6(self.output(y)) # range (0,6)\n", "\n", " return y" ] }, { "cell_type": "markdown", "metadata": { "id": "Rqu9qwp11HVW" }, "source": [ "We normalize the inputs and states" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "d3z_jDflAXTq" }, "outputs": [], "source": [ "# normalization for states and actions \n", "data_res['x_norm'] = np.array([[.8, 315,0, 0],[.1, 10,.1, 20]]) # [mean],[range]\n", "data_res['u_norm'] = np.array([[10/6],[295]]) # [range/6],[bias]" ] }, { "cell_type": "markdown", "metadata": { "id": "YfVTO6k6F_Bh" }, "source": [ "Now, let's create the objective function for the policy network. \n", "\n", "```{tip} Notice the difference between this objective function and the objective function use in [tutorial 6](https://edgarsmdn.github.io/MLCE_book/04_DNN_VLE.html). We have included a conditional to switch between algorithms.\n", "```\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oEEm8MD9FvvW" }, "outputs": [], "source": [ "def J_PolicyCSTR(policy, data_res=data_res, policy_alg='PID', \n", " collect_training_data=True, traj=False, episode=False):\n", " \n", " # load data\n", " Ca = copy.deepcopy(data_res['Ca_dat'])\n", " T = copy.deepcopy(data_res['T_dat'])\n", " Tc = copy.deepcopy(data_res['Tc_dat'])\n", " t = copy.deepcopy(data_res['t']) \n", " x0 = copy.deepcopy(data_res['x0']) \n", " noise = data_res['noise']\n", " \n", " # setpoints \n", " Ca_des = data_res['Ca_des']; T_des = data_res['T_des']\n", " \n", " # upper and lower bounds\n", " Tc_ub = data_res['Tc_ub']; Tc_lb = data_res['Tc_lb']\n", " \n", " # normalized states and actions\n", " x_norm = data_res['x_norm']; u_norm = data_res['u_norm'];\n", "\n", " # initiate\n", " x = x0\n", " e_history = []\n", "\n", " # log probs\n", " if policy_alg == 'PG_RL':\n", " log_probs = [None for i in range(len(t)-1)]\n", " \n", " # Simulate CSTR\n", " for i in range(len(t)-1):\n", " # delta t\n", " ts = [t[i],t[i+1]]\n", " # desired setpoint\n", " x_sp = np.array([Ca_des[i],T_des[i]])\n", "\n", " #### PID ####\n", " if policy_alg == 'PID':\n", " if i == 0:\n", " Tc[i] = PID(policy, x, x_sp, np.array([[0,0]]))\n", " else:\n", " Tc[i] = PID(policy, x, x_sp, np.array(e_history))\n", "\n", " # --------------> New compared to tutorial 6 <-------------------\n", " #### Stochastic Policy Search ####\n", " elif policy_alg == 'SPS_RL':\n", " xk = np.hstack((x,x_sp-x))\n", " # state preprocesing\n", " xknorm = (xk-x_norm[0])/x_norm[1]\n", " xknorm_torch = torch.tensor(xknorm)\n", " # compute u_k from policy\n", " mean_uk = policy(xknorm_torch).detach().numpy()\n", " u_k = np.reshape(mean_uk, (1, 1))\n", " u_k = u_k*u_norm[0] + u_norm[1]\n", " u_k = u_k[0]\n", " u_k = min(max(u_k, Tc_lb), Tc_ub)\n", " Tc[i] = u_k[0]\n", "\n", " #### Policy Gradients #### \n", " # See next section for the explanation on Policy gradients!\n", " elif policy_alg == 'PG_RL':\n", " xk = np.hstack((x,x_sp-x))\n", " # state preprocesing\n", " xknorm = (xk-x_norm[0])/x_norm[1]\n", " xknorm_torch = Tensor(xknorm)\n", " # compute u_k distribution\n", " m, s = policy(xknorm_torch)[0,0]\n", " s = s + eps\n", " mean_uk, std_uk = mean_std(m, s)\n", " u_k, logprob_k, entropy_k = select_action(mean_uk, std_uk)\n", " u_k = np.reshape(u_k.numpy(), (nu))\n", " # hard bounds on inputs\n", " u_k = min(max(u_k, Tc_lb), Tc_ub)\n", " Tc[i] = u_k\n", " log_probs[i] = logprob_k\n", " # ----------------------------------------------------------------\n", "\n", " # simulate system\n", " y = odeint(cstr,x,ts,args=(Tc[i],))\n", " # add process disturbance\n", " s = np.random.uniform(low=-1, high=1, size=2)\n", " Ca[i+1] = y[-1][0] + noise*s[0]*0.1 \n", " T[i+1] = y[-1][1] + noise*s[1]*5 \n", " # state update\n", " x[0] = Ca[i+1]\n", " x[1] = T[i+1]\n", " # compute tracking error\n", " e_history.append((x_sp-x))\n", "\n", " # == objective == #\n", " # tracking error\n", " error = np.abs(np.array(e_history)[:,0])/0.2+np.abs(np.array(e_history)[:,1])/15\n", " # penalize magnitud of control action\n", " u_mag = np.abs(Tc[:]-Tc_lb)/10\n", " u_mag = u_mag/10\n", " # penalize change in control action\n", " u_cha = np.abs(Tc[1:]-Tc[0:-1])/10\n", " u_cha = u_cha/10\n", "\n", " # collect data for plots\n", " if collect_training_data:\n", " data_res['Ca_train'].append(Ca)\n", " data_res['T_train'].append(T)\n", " data_res['Tc_train'].append(Tc)\n", " data_res['err_train'].append(error)\n", " data_res['u_mag_train'].append(u_mag)\n", " data_res['u_cha_train'].append(u_cha)\n", "\n", " # sums\n", " error = np.sum(error)\n", " u_mag = np.sum(u_mag)\n", " u_cha = np.sum(u_cha)\n", "\n", " if episode:\n", " # See next section for the explanation on Policy gradients!\n", " sum_logprob = sum(log_probs)\n", " reward = -(error + u_mag + u_cha)\n", " return reward, sum_logprob\n", " \n", " if traj:\n", " return Ca, T, Tc\n", " else:\n", " return error + u_mag + u_cha" ] }, { "cell_type": "markdown", "metadata": { "id": "zE9JcwV9L45N" }, "source": [ "As mentioned above, we are going to use a stochastic search algorithm that combines random search with local random search to optimize the policy network.\n", "\n", "The code below has two main elements:\n", "\n", "**Random Search Step**: During this step neural network weights are sampled uniformely (given some bounds). Each set of parameters is evaluated (a simulation is run), and the parameter set that performed best is passed to the next step.\n", "\n", "An illustration of how Random Search would look in a 2-dimensional space is shown below\n", "\n", "```{figure} media/07_RL/random_search.PNG\n", ":alt: kNN\n", ":width: 75%\n", ":align: center\n", "\n", "An illustration of the random search algorithm. [source](https://commons.wikimedia.org/wiki/File:Hyperparameter_Optimization_using_Random_Search.svg)\n", "```\n", "\n", "**Local Search Step**: This step starts from the best parameter values found by the *Random Search Step*. Subsequently it does a random search close by the best value found (hence termed stocastic local search). Additionally, if after some number of interations a better function value has not been found (also via simulating the system), the radius of search is reduced. \n", "\n", "An illustration of how Local search would look in a 2-dimensional space is shown below\n", "\n", "```{figure} media/07_RL/local_random_search.PNG\n", ":alt: kNN\n", ":width: 70%\n", ":align: center\n", "\n", "An illustration of the local search algorithm.\n", "```\n", "\n", "By combining a 'global search' strategy (Random search) and a 'local search' strategy (Local search) we balance exploration and exploitation which is a key concept in reinforcement learning." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bCQhGNmCMB6V" }, "outputs": [], "source": [ "#######################\n", "# auxiliary functions #\n", "#######################\n", "\n", "def sample_uniform_params(params_prev, param_max, param_min):\n", " params = {k: torch.rand(v.shape)* (param_max - param_min) + param_min \\\n", " for k, v in params_prev.items()} \n", " return params\n", "\n", "def sample_local_params(params_prev, param_max, param_min):\n", " params = {k: torch.rand(v.shape)* (param_max - param_min) + param_min + v \\\n", " for k, v in params_prev.items()} \n", " return params\n", "\n", "#################################\n", "# Generalized policy search \n", "#################################\n", "\n", "def Generalized_policy_search(shrink_ratio=0.5, radius=0.1, evals_shrink=1, \n", " evals=12, ratio_ls_rs=0.3):\n", " '''\n", " Tailores to address function: J_BB_bioprocess(model, dt, x0, Umax, n_run, n_steps)\n", " bounds: np.array([150,7])\n", " '''\n", "\n", " # adapt evaluations \n", " evals_rs = round(evals*ratio_ls_rs)\n", " evals_ls = evals - evals_rs\n", "\n", " # problem initialisation\n", " nu = 1\n", " nx = 2\n", " hyparams = {'input_size': nx+2, 'output_size': nu} # include setpoints +2\n", " n_steps = data_res['n']\n", "\n", " #######################\n", " # policy initialization\n", " #######################\n", "\n", " policy_net = Net(**hyparams)\n", " params = policy_net.state_dict()\n", " param_max = 5# 1.5\n", " param_min = -5#-1.5\n", "\n", " # == initialise rewards == #\n", " best_reward = 1e8\n", " best_policy = copy.deepcopy(params) \n", "\n", " ###############\n", " # Random search\n", " ###############\n", "\n", " for policy_i in range(evals_rs):\n", " # == Random Search in policy == #\n", " # sample a random policy\n", " NNparams_RS = sample_uniform_params(params, param_max, param_min)\n", " # consrict policy to be evaluated\n", " policy_net.load_state_dict(NNparams_RS)\n", " # evaluate policy\n", " reward = J_PolicyCSTR(policy_net, collect_training_data=True, \n", " policy_alg='SPS_RL')\n", " # benchmark reward ==> min \"<\"\n", " if reward < best_reward:\n", " best_reward = reward\n", " best_policy = copy.deepcopy(NNparams_RS) \n", "\n", " ###############\n", " # local search\n", " ###############\n", "\n", " # define max radius\n", " r0 = np.array([param_min, param_max])*radius\n", "\n", " # initialization\n", " iter_i = 0\n", " fail_i = 0\n", "\n", " while iter_i < evals_ls:\n", "\n", " # shrink radius\n", " if fail_i >= evals_shrink:\n", " fail_i = 0\n", " radius = radius*shrink_ratio\n", " r0 = np.array([param_min, param_max])*radius\n", "\n", " # new parameters\n", " NNparams_LS = sample_local_params(best_policy, r0[1], r0[0])\n", "\n", " # == bounds adjustment == #\n", " \n", " # evaluate new agent\n", " policy_net.load_state_dict(NNparams_LS)\n", " reward = J_PolicyCSTR(policy_net, collect_training_data=True, \n", " policy_alg='SPS_RL') \n", "\n", " # choose the == Min == value \n", " if reward < best_reward:\n", " best_reward = reward\n", " best_policy = copy.deepcopy(NNparams_LS)\n", " fail_i = 0\n", " else:\n", " fail_i += 1\n", "\n", " # iteration counter\n", " iter_i += 1 \n", "\n", " print('final reward = ',best_reward)\n", " print('radius = ',radius)\n", " return best_policy, best_reward\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KVTexkPHtQAt", "outputId": "d22755a8-655e-4f58-ec54-ec2b760fe7fb" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "final reward = 18.903103565888948\n", "radius = 0.013508517176729932\n" ] } ], "source": [ "# data for plots\n", "data_res['Ca_train'] = []; data_res['T_train'] = [] \n", "data_res['Tc_train'] = []; data_res['err_train'] = []\n", "data_res['u_mag_train'] = []; data_res['u_cha_train'] = []\n", "\n", "# problem parameters\n", "e_tot = 500\n", "e_shr = e_tot/30\n", "\n", "# Policy optimization\n", "best_policy, best_reward = \\\n", "Generalized_policy_search(shrink_ratio=0.9, radius=0.1, evals_shrink=e_shr,\n", " evals=e_tot, ratio_ls_rs=0.1)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 507 }, "id": "uKP2cgYYP4Wl", "outputId": "ef376612-0ad7-4c3b-f75f-9bac077ad461" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_training(data_res, e_tot)" ] }, { "cell_type": "markdown", "metadata": { "id": "CAttD3gQU73I" }, "source": [ "Notice that the 'input size' below has an extra +2. This is because we must pass the information to the policy network about the setpoint (given that our setpoint will change). Therefore, we give 2 extra inputs to our policy network, one for each setpoint." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 506 }, "id": "KNuR5hHRTJbV", "outputId": "24befdf1-5e76-441d-decf-f94529393c94" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nx = 2\n", "nu = 1\n", "hyparams = {'input_size': nx+2, 'output_size': nu} # include setpoints +2\n", "policy_net_SPS_RL = Net(**hyparams, requires_grad=True, retain_graph=True)\n", "policy_net_SPS_RL.load_state_dict(best_policy)\n", "\n", "reps = 10\n", "\n", "Ca_eval = np.zeros((data_res['Ca_dat'].shape[0], reps))\n", "T_eval = np.zeros((data_res['T_dat'].shape[0], reps))\n", "Tc_eval = np.zeros((data_res['Tc_dat'].shape[0], reps))\n", "\n", "for r_i in range(reps):\n", " Ca_eval[:,r_i], T_eval[:,r_i], Tc_eval[:,r_i] = J_PolicyCSTR(policy_net_SPS_RL,\n", " policy_alg='SPS_RL', \n", " collect_training_data=False, \n", " traj=True)\n", "# Plot the results\n", "plot_simulation(Ca_eval, T_eval, Tc_eval, data_res)" ] }, { "cell_type": "markdown", "metadata": { "id": "fK2bhjxBHUt2" }, "source": [ "### Remarks on stochastic policy search\n", "\n", "As it can be observed from the simple example above Stochastic local search methods work well in practice and are much easier to implement that other techniques (such as policy gradients). In general, as we move to large parameter spaces, for example neural networks with millions of parameters, their performance will deteriorate, and a policy gradient-like approach will be more desireable. " ] }, { "cell_type": "markdown", "metadata": { "id": "12kXL7R66MvR" }, "source": [ "## Policy Gradients 🗻" ] }, { "cell_type": "markdown", "metadata": { "id": "xGG2WsnA9k9e" }, "source": [ "Policy gradient methods rely on the [Policy Gradient Theorem](https://link.springer.com/article/10.1007/BF00992696) which allows to retrieve the gradient of the expected objective function with respect to policy parameters (neural network weights) $\\nabla_\\theta \\mathbb{E}_{\\pi_\\theta}[f(\\pi_\\theta)]$ through Monte Carlo runs. Given the availability of gradients, it is possible to follow a gradient-based optimization to optimize the policy, generally, Adam or Gradient Descent is used. More information can be found of Chapter 13 on [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/the-book.html). \n", "\n", "In this tutorial notebook we outline the simplest algorithm of this kind, **Reinforce**. For a more in dept introduction to the topic see [Intro to Policy Optimization](https://spinningup.openai.com/en/latest/spinningup/rl_intro3.html).\n", "\n", "In the case of stochastic policies, the policy function returns the defining parameters of a probability distribution (such as the mean and variance) over possible actions, from which the actions are sampled: \n", "$$\\textbf{u} \\sim \\pi_\\theta(\\textbf{u} | \\textbf{x}) = \\pi(\\textbf{u} | \\textbf{x}, \\theta) = p(\\textbf{u} | \\textbf{x}, \\theta)$$\n", "\n", "Note*: this is the same as the stochastic policy introduced earlier, following the 'probability notation'.\n", "\n", "To learn the optimal policy, we seek to maximize our performance metric, and hence we can follow a gradient ascent strategy: \n", "$$\\theta_{m+1} = \\theta_m + \\alpha_m \\nabla_\\theta \\mathbb{E}_{\\pi_\\theta}[f(\\pi_\\theta)] $$\n", "\n", "where $m$ is the current iteration that the parameters are updated (epoch), $\\nabla_\\theta \\mathbb{E}_{\\pi_\\theta}[f(\\pi_\\theta)]$ is the expectation of $f$ over $\\pi_\\theta$ and $\\alpha_m$ is the step size (also termed learning rate in the RL community) for the $m^{th}$ iteration. \n", "\n", "Computing $ \\nabla_\\theta \\hat{f}(\\theta) = \\nabla_\\theta\\mathbb{E}_{\\pi_\\theta}[J(\\pi_\\theta)]$ directly is difficult, therefore we use the Policy Gradient Theorem, which shows the following:\n", "\n", "\n", "$$ \\begin{alignat}{3}\n", "\\nabla_\\theta\\hat{f}(\\theta) = \\nabla_\\theta \\mathbb{E}_{\\pi_\\theta}[f(\\pi_\\theta)] &~=~\\nabla_\\theta \\int p(\\pi_\\theta|\\theta)~ f(\\pi_\\theta)\\text{d}\\pi_\\theta\\\\\n", "&~=~\\int \\nabla_\\theta p(\\pi_\\theta|\\theta)~ f(\\pi_\\theta) \\text{d}\\pi_\\theta\\\\\n", "&~=~\\int p(\\pi_\\theta|\\theta) ~ \\frac{\\nabla_\\theta p(\\pi_\\theta|\\theta)}{p(\\pi_\\theta|\\theta)~} ~ f(\\pi_\\theta) \\text{d}\\pi_\\theta\\\\\n", "&~=~\\int p(\\pi_\\theta|\\theta) ~ \\nabla_\\theta \\text{log}\\left( p(\\pi_\\theta|\\theta)\\right) f(\\pi_\\theta)\\text{d}\\pi_\\theta\\\\\n", "&~=~\\mathbb{E}_{\\pi_\\theta} \\left[ f(\\pi_\\theta) \\nabla_\\theta \\text{log}\\left( p(\\pi_\\theta|\\theta)\\right) \\right]\n", "\\end{alignat} $$\n", "\n", "Notice from the first expression, that, $p(\\pi_\\theta|\\theta)~ f(\\pi_\\theta)$ is an objective function value multiplied by its probability density, therefore, integrating this over all possible values of $\\pi_\\theta$ we obtain the expected value. From there, using simple algebra, logarithms and the chain rule, we arrive at the final equations, which shows an expected value, where, dropping the explicit distribution of $\\pi_\\theta$, gives us an unbiased gradient estimator, our steepest ascent update now becomes:\n", "\n", "$$ \\theta_{m+1} = \\theta_m + \\alpha_m \\mathbb{E}_{\\pi_\\theta}\\left[ f(\\pi_\\theta) \\nabla_\\theta \\text{log}\\left( p(\\pi_\\theta|\\theta)\\right) \\right] \n", "$$\n", "\n", "Using the expression for $p(\\pi_\\theta|\\theta) = \\hat{\\mu}(\\textbf{x}_0) \\prod_{t=0}^{T-1} \\left[\\pi(\\textbf{u}_t|\\textbf{x}_t,\\theta) p(\\textbf{x}_{t+1}|\\textbf{x}_t,\\textbf{u}_t) \\right]$ and taking its logarithm, we obtain:\n", "\n", "$$\\nabla_\\theta \\text{log}\\left( p(\\pi_\\theta|\\theta)\\right) = \\nabla_\\theta \\sum_{t=0}^{T-1} \\text{log}\\left( \\pi(\\textbf{u}_t|\\textbf{x}_t,\\theta)\\right)$$\n", "\n", "Note that since $p(\\textbf{x}_{t+1}|\\textbf{x}_t,\\textbf{u}_t)$ and $\\hat{\\mu}(\\textbf{x}_0)$ are independent of $\\theta$ they disappear from the above expression. Then we can rewrite for a trajectory as:\n", "\n", "$$\\nabla_\\theta \\mathbb{E}_{\\pi_\\theta}[f(\\pi_\\theta)] = \\mathbb{E}_{\\pi_\\theta} \\left[ f(\\pi_\\theta) \\nabla_\\theta \\sum_{t=0}^{T-1} \\text{log}\\left( \\pi(\\textbf{u}_t|\\textbf{x}_t,\\theta)\\right)\\right]$$\n", "\n", "The above expression does not require the knowledge of the dynamics of the physical system. Monte-Carlo method is utilized to approximate the expectation.\n", "\n", "$$\\nabla_\\theta \\mathbb{E}_{\\pi_\\theta}[f(\\pi_\\theta)] \\approx \\frac{1}{K} \\sum_{k=1}^{K} \\left( f(\\pi_\\theta) \\nabla_\\theta \\sum_{t=0}^{T-1} \\text{log}\\left( \\pi(\\textbf{u}_t|\\textbf{x}_t,\\theta)\\right)\\right)$$\n", "\n", "\n", "**Reinforce algorithm**\n", "\n", "${\\bf Input:}$ Initialize policy parameter $\\theta = \\theta_0$, with $\\theta_0\\in\\Theta_0$, learning rate $\\alpha$, set number of episodes $K$ and number of epochs $N$.\n", "\n", "${\\bf Output:}$ policy $\\pi(\\cdot | \\cdot ,\\theta)$ and $\\Theta$\n", "\\smallskip\n", "\n", "${\\bf for}$ $m = 1,\\dots, N$ ${\\bf do}$\n", "\n", "\n", "1. Collect $\\textbf{u}_t^k , \\textbf{x}_t^k,f(\\pi_\\theta^k)$ for $K$ trajectories of $T$ time steps.\n", "2. Estimate the gradient $ \\hat{g}_m := \\frac{1}{K} \\sum_{k=1}^{K} \\left[ f(\\pi_\\theta^k) \\nabla_\\theta \\sum_{t=0}^{T-1} \\text{log}\\left( \\pi(\\textbf{u}_t^k|\\hat{\\textbf{x}}_t^k,\\theta)\\right)\\right]$\n", "3. Update the policy using a policy gradient estimate $\\theta_{m+1} = \\theta_m + \\alpha_m \\hat{g}_m$\n", "4. $m:=m+1$\n", "\n", "${\\bf Remark}$: The above algorithm is the base version for many further developments that have been made since it was first proposed. \n", "\n", "The steps of the algorithm are explained below:\n", "\n", "${\\bf Initialization:}$ The policy network and its weights $\\theta$ are initialized along with the algorithm's hyperparameters such as learning rate, number of episodes and number of epochs.\n", "\n", "${\\bf Training ~~ loop:}$ The weights of the policy network are updated by a policy gradient scheme for a total of $N$ epochs. \n", "\n", "In ${\\bf Step 1}$ $K$ trajectories are computed, each trajectory consists of $T$ time steps, and states and control actions are collected. \n", "\n", "In ${\\bf Step 2}$ the gradient of the objective function with respect to the weights for the policy network is computed. \n", "\n", "In ${\\bf Step 3}$ the weights of the policy network are updated by a gradient ascent scheme. Note that here we show a steepest ascent-like update, but other first order (i.e. Adam) or trust region methods can be used (i.e. PPO, TRPO). \n", "\n", "In ${\\bf Step 4}$, either the algorithm terminates or returns to Step 1." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xJcQK37MU8uT" }, "outputs": [], "source": [ "import torch.nn as nn\n", "import torch.optim as optim\n", "from torch import Tensor\n", "from torch.distributions import MultivariateNormal" ] }, { "cell_type": "markdown", "metadata": { "id": "irpHRfdQA5xQ" }, "source": [ "In order to implement and apply the Reinforce algorithm, we are going to perform the following steps in the following subsections:\n", "\n", "\n", "* Create a [policy network](#policy_net) that uses transfer learning\n", "* Create an auxiliary function that selects [control actions](#control_actions) out of the distribution\n", "* Create an auxilary function that runs [multiple episodes](#multi_episodes) per epoch\n", "* Finally, put all the pieces together into a function that computes the [Reinforce algorithm](#r_alg)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "wI2tR44uq1V-" }, "source": [ "\n", "### Policy network\n" ] }, { "cell_type": "markdown", "metadata": { "id": "tfgwJOezjlJk" }, "source": [ "In the next section of this tutorial notebook we show an implementation from scratch of a policy optimization algorithm using policy gradients.\n", "\n", "Given that we have already optimized the policy via stochastic search in the previous section, we use those parameters as a starting point for policy gradients. This is a form of [transfer learning](https://en.wikipedia.org/wiki/Transfer_learning#:~:text=Transfer%20learning%20(TL)%20is%20a,when%20trying%20to%20recognize%20trucks.), which refers to applying knowledge that was previously gained while solving one task to a related task. \n", "\n", "Related to this transfer learning process, we will create a second neural network with the exact same configuration as before, but we will add an extra node to each output of the original neural network to account for the variance term. This is because previously our neural network policy had the structure:\n", "\n", "$${\\bf u}:=\\pi({\\bf x};\\boldsymbol{\\theta})$$\n", "\n", "and by a Stochastic search algorithm we manipulated the weights $\\boldsymbol{\\theta}$ to optimize performance. In policy gradients we output a distribution, rathen than a single value, in this case we output the mean and the variance of a normal distribution (each output now has two values, the mean and the variance), and therefore we must add an extra node to each output such that we have:\n", "\n", "$$ \\boldsymbol{ \\mu }, \\boldsymbol{ \\Sigma } := \\pi({\\bf x};\\boldsymbol{\\theta})$$\n", "\n", "$${\\bf u} \\sim \\mathcal{N}(\\boldsymbol{ \\mu }, \\boldsymbol{ \\Sigma })$$\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BF-2QR1ulVVb" }, "outputs": [], "source": [ "#########################################\n", "# Policy Network with transfer learning #\n", "#########################################\n", "\n", "class Net_TL(torch.nn.Module):\n", " # in current form this is a linear function (wouldn't expect great performance here)\n", " def __init__(self, **kwargs):\n", " super(Net_TL, self).__init__()\n", "\n", " self.dtype = torch.float\n", "\n", " # Unpack the dictionary \n", " self.args = kwargs\n", "\n", " # Get info of machine\n", " self.use_cuda = torch.cuda.is_available() \n", " self.device = torch.device(\"cpu\")\n", "\n", " # Define ANN topology \n", " self.input_size = self.args['input_size']\n", " self.output_sz = self.args['output_size']\n", " self.hs1 = self.input_size*2\n", " self.hs2 = self.output_sz*2 \n", "\n", " # Define layers \n", " self.hidden1 = torch.nn.Linear(self.input_size, self.hs1 )\n", " self.hidden2 = torch.nn.Linear(self.hs1, self.hs2)\n", " self.output = torch.nn.Linear(self.hs2, self.output_sz)\n", "\n", " def forward(self, x):\n", " #x = torch.tensor(x.view(1,1,-1)).float() # re-shape tensor\n", " x = x.view(1, 1, -1).float()\n", " y = Ffunctional.leaky_relu(self.hidden1(x), 0.1)\n", " y = Ffunctional.leaky_relu(self.hidden2(y), 0.1)\n", " y = Ffunctional.relu6(self.output(y)) # range (0,6)\n", "\n", " return y\n", "\n", " def increaseClassifier(self, m:torch.nn.Linear):\n", " w = m.weight\n", " b = m.bias\n", " old_shape = m.weight.shape\n", "\n", " m2 = nn.Linear( old_shape[1], old_shape[0] + 1)\n", " m2.weight = nn.parameter.Parameter( torch.cat( (m.weight, m2.weight[0:1]) ), \n", " requires_grad=True )\n", " m2.bias = nn.parameter.Parameter( torch.cat( (m.bias, m2.bias[0:1]) ), \n", " requires_grad=True)\n", " return m2\n", " \n", " def incrHere(self): \n", " self.output = self.increaseClassifier(self.output)" ] }, { "cell_type": "markdown", "metadata": { "id": "bhyvC7tXsAC3" }, "source": [ "Notice that just as before, we ad a +2 to the input, as thisrefers to the current set-point value." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6O4rW7Ug_Vjz" }, "outputs": [], "source": [ "nx = 2\n", "nu = 1\n", "hyparams = {'input_size': nx+2, 'output_size': nu} # include setpoints +2\n", "\n", "policy_net_pg = Net_TL(**hyparams, requires_grad=True, retain_graph=True)\n", "policy_net_pg.load_state_dict(best_policy) # Transfer learning\n", "policy_net_pg.incrHere()" ] }, { "cell_type": "markdown", "metadata": { "id": "iXYpqaMjwMhC" }, "source": [ "\n", "### Control action selection\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XbovPCpowOYU" }, "outputs": [], "source": [ "################################\n", "# action selection from Normal #\n", "################################\n", "\n", "def select_action(control_mean, control_sigma):\n", " \"\"\"\n", " Sample control actions from the distribution their distribution\n", " input: Mean, Variance\n", " Output: Controls, log_probability, entropy\n", " \"\"\"\n", " s_cov = control_sigma.diag()**2\n", " dist = MultivariateNormal(control_mean, s_cov)\n", " control_choice = dist.sample() # sample control from N(mu,std)\n", " log_prob = dist.log_prob(control_choice) # compute log prob of this action (how likely or unlikely)\n", " entropy = dist.entropy() # compute the entropy of the distribution N(mu, std)\n", " \n", " return control_choice, log_prob, entropy\n", "\n", "#########################\n", "# un-normalizing action #\n", "#########################\n", "\n", "def mean_std(m, s, mean_range=[10], mean_lb=[295], std_range=[0.001]):\n", " '''\n", " Problem specific restrictions on predicted mean and standard deviation.\n", " '''\n", " mean = Tensor(mean_range) * m/6 + Tensor(mean_lb) # ReLU6\n", " std = Tensor(std_range) * s/6\n", " \n", " return mean, std" ] }, { "cell_type": "markdown", "metadata": { "id": "UaynDJOxBlRG" }, "source": [ "\n", "### Multiple episodes - one epoc/training step\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "sPoiETuSBt83" }, "outputs": [], "source": [ "################\n", "# one epoc run #\n", "################\n", "\n", "def epoc_run(NNpolicy, episodes_n):\n", " '''This function runs episodes_n episodes and collected the data. This data\n", " is then used for one gradient descent step.\n", "\n", " INPUTS\n", " NNpolicy: the NN policy\n", " episodes_n: number of episodes per epoc (gradient descent steps)\n", " data_train: dictionary of data collected\n", "\n", " OUTPUTS\n", " data_train: collected data to be passed to the main training loop\n", " '''\n", "\n", " # run episodes\n", " logprobs_list = [] # log probabilities is the policies itself p(a|s)\n", " reward_list = [] # reward\n", " for epi_i in range(episodes_n):\n", " reward_, sum_logprob = J_PolicyCSTR(NNpolicy, policy_alg='PG_RL', \n", " collect_training_data=True, episode=True)\n", " logprobs_list.append(sum_logprob)\n", " reward_list.append(reward_)\n", "\n", " # compute mean and expectation of rewards\n", " reward_m = np.mean(reward_list)\n", " reward_std = np.std(reward_list)\n", " \n", "\n", " # compute the baseline (reverse sum)\n", " log_prob_R = 0.0\n", " for epi_i in reversed(range(episodes_n)):\n", " baselined_reward = (reward_list[epi_i] - reward_m) / (reward_std + eps)\n", " log_prob_R = log_prob_R - logprobs_list[epi_i] * baselined_reward\n", "\n", " # mean log probability\n", " mean_logprob = log_prob_R/episodes_n\n", " reward_std = reward_std\n", " reward_m = reward_m\n", "\n", " return mean_logprob, reward_std, reward_m " ] }, { "cell_type": "markdown", "metadata": { "id": "jNRye3WMUtxQ" }, "source": [ "\n", "### Reinforce algorithm\n", "\n", "Now, let's create a function that put all pieces together and implements the Reinforce algorithm explained above" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LhFzvmZHUvZt" }, "outputs": [], "source": [ "def Reinforce(policy, optimizer, n_epochs, n_episodes):\n", "\n", " # lists for plots\n", " rewards_m_record = []; rewards_std_record = []\n", "\n", " for epoch_i in range(n_epochs):\n", "\n", " # collect data\n", " mean_logprob, reward_std, reward_m = epoc_run(policy, n_episodes)\n", "\n", " # Expected log reward \n", " E_log_R = mean_logprob\n", " optimizer.zero_grad()\n", " E_log_R.backward()\n", " optimizer.step()\n", "\n", " # save data for analysis\n", " rewards_m_record.append(reward_m)\n", " rewards_std_record.append(reward_std)\n", "\n", " # schedule to reduce lr\n", " scheduler.step(E_log_R)\n", "\n", " if epoch_i%int(n_epochs/10)==0:\n", " mean_r = reward_m\n", " std_r = reward_std\n", " print('epoch:', epoch_i)\n", " print(f'mean reward: {mean_r:.3} +- {std_r:.2}')\n", "\n", " return rewards_m_record, rewards_std_record, policy" ] }, { "cell_type": "markdown", "metadata": { "id": "vnX42ydtDK29" }, "source": [ "### Apply the Reinforce algorithm\n", "\n", "Now that we have the algorithm, let's apply it to the problem. \n", "\n", "Let's choose some problem parameters and initialize storing lists" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DJSfwqm2JE4Z" }, "outputs": [], "source": [ "# problem parameters\n", "lr = 0.0001\n", "total_it = 2000\n", "n_episodes = 50\n", "n_epochs = int(total_it/n_episodes)\n", "\n", "# data for plots\n", "data_res['Ca_train'] = []; data_res['T_train'] = [] \n", "data_res['Tc_train'] = []; data_res['err_train'] = []\n", "data_res['u_mag_train'] = []; data_res['u_cha_train'] = []" ] }, { "cell_type": "markdown", "metadata": { "id": "N28px72QJFnb" }, "source": [ "define the policy and the optimizer to be used" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "GyEtyEzUCO8p" }, "outputs": [], "source": [ "# define policy and optimizer\n", "control_policy = policy_net_pg\n", "optimizer_pol = optim.Adam(control_policy.parameters(), lr=lr)" ] }, { "cell_type": "markdown", "metadata": { "id": "9D16tIchJU1F" }, "source": [ "define the learning rate scheduler" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XbyqhFV5JX--" }, "outputs": [], "source": [ "# Define Scheduler\n", "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n", " optimizer_pol, factor=0.5, patience=10, verbose=True, min_lr=0.000001,\n", " cooldown = 10)" ] }, { "cell_type": "markdown", "metadata": { "id": "omrMJ17fJdv4" }, "source": [ "and apply the algorithm\n", "\n", "```{note} Runing the algorithm will last for a few minutes\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NAd0UTLQJiM_", "outputId": "219f6eea-effc-40cd-b952-e41dd4155bd2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "epoch: 0\n", "mean reward: -21.6 +- 1.1\n", "epoch: 4\n", "mean reward: -22.0 +- 1.3\n", "epoch: 8\n", "mean reward: -21.9 +- 1.2\n", "epoch: 12\n", "mean reward: -21.7 +- 1.0\n", "epoch: 16\n", "mean reward: -21.8 +- 1.1\n", "Epoch 00021: reducing learning rate of group 0 to 5.0000e-05.\n", "epoch: 20\n", "mean reward: -21.5 +- 1.2\n", "epoch: 24\n", "mean reward: -22.0 +- 1.3\n", "epoch: 28\n", "mean reward: -21.9 +- 1.2\n", "epoch: 32\n", "mean reward: -21.8 +- 1.1\n", "epoch: 36\n", "mean reward: -21.7 +- 1.1\n" ] } ], "source": [ "rewards_m_record, rewards_std_record, optimal_Reinforce = \\\n", "Reinforce(control_policy, optimizer_pol, n_epochs, n_episodes)" ] }, { "cell_type": "markdown", "metadata": { "id": "_MUcfPGiJ26q" }, "source": [ "Let's visualize now" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 452 }, "id": "YkKIhhy7TVCq", "outputId": "08b72862-0799-4c03-ec16-267608a4ff1c" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot the samples of posteriors\n", "plt.plot(rewards_m_record, 'black', linewidth=1)\n", "# plot GP confidence intervals\n", "iterations = [i for i in range(len(rewards_m_record))]\n", "plt.gca().fill_between(iterations, np.array(rewards_m_record) - 3*np.array(rewards_std_record), \n", " np.array(rewards_m_record) + 3*np.array(rewards_std_record), \n", " color='C0', alpha=0.2)\n", "plt.title('RL Reward')\n", "plt.legend(('mean', 'conf interval'),\n", " loc='lower right')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 507 }, "id": "oKcQuKg-Lv69", "outputId": "0a42767d-c9f3-46ff-b7c1-7889293cdcf1" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_training(data_res, e_tot)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 507 }, "id": "eVcEZeeV-7og", "outputId": "27946721-1766-4497-b627-94b141ceb7ca" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "reps = 10\n", "\n", "Ca_eval = np.zeros((data_res['Ca_dat'].shape[0], reps))\n", "T_eval = np.zeros((data_res['T_dat'].shape[0], reps))\n", "Tc_eval = np.zeros((data_res['Tc_dat'].shape[0], reps))\n", "\n", "for r_i in range(reps):\n", " Ca_eval[:,r_i], T_eval[:,r_i], Tc_eval[:,r_i] = J_PolicyCSTR(policy_net_pg,\n", " policy_alg='PG_RL', \n", " collect_training_data=False, \n", " traj=True)\n", "# Plot the results\n", "plot_simulation(Ca_eval, T_eval, Tc_eval, data_res)" ] }, { "cell_type": "markdown", "metadata": { "id": "INKJIJhF3J1D" }, "source": [ "## Extra material on RL for ChemEng 🤓\n", "\n", "If you would like to read more about the use of reinforcement learning in chemical engineering systems:\n", "\n", "**Applications** \n", "\n", "* Reinforcement learning offers potential for bringing significant improvements to [industrial batch process control practice](https://www.sciencedirect.com/science/article/abs/pii/S136757882100081X) even in [discontinous and nonlinear systems](https://www.sciencedirect.com/science/article/abs/pii/S0098135419304168).\n", "* RL has also been used to address [chemical production scheduling](https://www.sciencedirect.com/science/article/pii/S0098135420301599) and [multi-echelon supply chains](https://www.sciencedirect.com/science/article/pii/S2772508122000643)\n", "* Other applications include [PID tuning](https://www.sciencedirect.com/science/article/abs/pii/S0967066121002963), [real-time optimization](https://www.mdpi.com/2227-9717/11/1/123), [searching for optimal process routes](https://www.sciencedirect.com/science/article/abs/pii/S0098135420303999), [flowsheet generation](https://aiche.onlinelibrary.wiley.com/doi/full/10.1002/aic.17938), [bioreactors](https://www.sciencedirect.com/science/article/abs/pii/S0098135419304168) and [biotherapeutics](https://onlinelibrary.wiley.com/doi/10.1002/bit.28346), amongst many others.\n", "\n", "**Methodologies** \n", "\n", "* Constraints to address [plant-model mismatch](https://www.sciencedirect.com/science/article/abs/pii/S0098135421004087), [constrained Q-learning](https://www.sciencedirect.com/science/article/abs/pii/S0098135421002404), [safe reinforcement learning](https://www.researchgate.net/publication/368302457_Safe_deployment_of_reinforcement_learning_using_deterministic_optimization_of_trained_neural_networks), satisfaction of constraints with [high probability](https://www.sciencedirect.com/science/article/pii/S0959152422000038), and [dynamic penalties](https://www.sciencedirect.com/science/article/pii/S0959152422000816) for better convergence.\n", "* [Process control](https://www.mdpi.com/2227-9717/10/11/2311), [meta-reinforcement learning](https://www.sciencedirect.com/science/article/pii/S0959152422001445), [general economic process control](https://www.sciencedirect.com/science/article/pii/S0098135420307912), amongst many many others. \n", "\n", " " ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.15" } }, "nbformat": 4, "nbformat_minor": 1 }