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Train Reinforcement Learning Policy Using Custom Training Loop

This example shows how to define a custom training loop for a reinforcement learning policy. You can use this workflow to train reinforcement learning policies with your own custom training algorithms rather than using one of the built-in agents from the Reinforcement Learning Toolbox™ software.

Using this workflow, you can train policies that use any of the following policy and value function approximators.

In this example, a discrete actor policy with a discrete action space is trained using the REINFORCE algorithm (with no baseline). For more information on the REINFORCE algorithm, see Policy Gradient Agents.

Fix the random generator seed for reproducibility.

rng(0)

For more information on the functions you can use for custom training, see Functions for Custom Training.

Environment

For this example, a reinforcement learning policy is trained in a discrete cart-pole environment. The objective in this environment is to balance the pole by applying forces (actions) on the cart. Create the environment using the rlPredefinedEnv function.

env = rlPredefinedEnv('CartPole-Discrete');

Extract the observation and action specifications from the environment.

obsInfo = getObservationInfo(env);
actInfo = getActionInfo(env);

Obtain the number of observations (numObs) and actions (numAct).

numObs = obsInfo.Dimension(1);
numAct = actInfo.Dimension(1);

For more information on this environment, see Load Predefined Control System Environments.

Policy

The reinforcement learning policy in this example is a discrete-action stochastic policy. It is represented by a deep neural network that contains fullyConnectedLayer, reluLayer, and softmaxLayer layers. This network outputs probabilities for each discrete action given the current observations. The softmaxLayer ensures that the actor outputs probability values in the range [0 1] and that all probabilities sum to 1.

Create the deep neural network for the actor.

actorNetwork = [featureInputLayer(numObs,'Normalization','none','Name','state')
                fullyConnectedLayer(24,'Name','fc1')
                reluLayer('Name','relu1')
                fullyConnectedLayer(24,'Name','fc2')
                reluLayer('Name','relu2')
                fullyConnectedLayer(2,'Name','output')
                softmaxLayer('Name','actionProb')];
actorNetwork = dlnetwork(actorNetwork)
actorNetwork = 
  dlnetwork with properties:

         Layers: [7×1 nnet.cnn.layer.Layer]
    Connections: [6×2 table]
     Learnables: [6×3 table]
          State: [0×3 table]
     InputNames: {'state'}
    OutputNames: {'actionProb'}
    Initialized: 1

Create the actor using an rlDiscreteCategoricalActor object.

actor = rlDiscreteCategoricalActor(actorNetwork,obsInfo,actInfo);

Accelerate the gradient computation of the actor.

actor = accelerate(actor,true);

Create the optimizer using rlOptimizer and rlOptimizerOptions function.

actorOpts = rlOptimizerOptions('LearnRate',1e-3);
actorOptimizer = rlOptimizer(actorOpts);

Training Setup

Configure the training to use the following options:

  • Set up the training to last at most 5000 episodes, with each episode lasting at most 250 steps.

  • To calculate the discounted reward, choose a discount factor of 0.995.

  • Terminate the training after the maximum number of episodes is reached or when the average reward across 100 episodes reaches the value of 220.

numEpisodes = 5000;
maxStepsPerEpisode = 250;
discountFactor = 0.995;
aveWindowSize = 100;
trainingTerminationValue = 230;

Create a vector for storing the cumulative reward for each training episode.

episodeCumulativeRewardVector = [];

Custom Training Loop

The algorithm for the custom training loop is as follows. For each episode:

  1. Reset the environment.

  2. Create buffers for storing experience information: observations, actions, and rewards.

  3. Generate experiences until a terminal condition occurs. To do so, evaluate the policy to get actions, apply those actions to the environment, and obtain the resulting observations and rewards. Store the actions, observations, and rewards in buffers.

  4. Collect the training data as a batch of experiences.

  5. Compute the episode Monte Carlo return, which is the discounted future reward.

  6. Compute the gradient of the loss function with respect to the policy parameters.

  7. Update the policy using the computed gradients.

  8. Update the training visualization.

  9. Terminate training if the policy is sufficiently trained.

doTraining = false;

if doTraining
    % Create a figure for training visualization using the hBuildFigure helper function.
    [trainingPlot,lineReward,lineAveReward] = hBuildFigure;
    % Enable the training visualization plot.
    set(trainingPlot,'Visible','on');

    % Train the policy for the maximum number of episodes or until the average
    % reward indicates that the policy is sufficiently trained.
    for episodeCt = 1:numEpisodes

        % 1. Reset the environment at the start of the episode
        obs = reset(env);

        episodeReward = zeros(maxStepsPerEpisode,1);

        % 2. Create buffers to store experiences. The dimensions for each buffer
        % must be as follows.
        %
        % For observation buffer:
        %     numberOfObservations x numberOfObservationChannels x batchSize
        %
        % For action buffer:
        %     numberOfActions x numberOfActionChannels x batchSize
        %
        % For reward buffer:
        %     1 x batchSize
        %
        observationBuffer = zeros(numObs,1,maxStepsPerEpisode);
        actionBuffer = zeros(numAct,1,maxStepsPerEpisode);
        rewardBuffer = zeros(1,maxStepsPerEpisode);

        % 3. Generate experiences for the maximum number of steps per
        % episode or until a terminal condition is reached.
        for stepCt = 1:maxStepsPerEpisode

            % Compute an action using the policy based on the current
            % observation.
            action = getAction(actor,{obs});
            action = action{1};

            % Apply the action to the environment and obtain the resulting
            % observation and reward.
            [nextObs,reward,isdone] = step(env,action);

            % Store the action, observation, and reward experiences in buffers.
            observationBuffer(:,:,stepCt) = obs;
            actionBuffer(:,:,stepCt) = action;
            rewardBuffer(:,stepCt) = reward;

            episodeReward(stepCt) = reward;
            obs = nextObs;

            % Stop if a terminal condition is reached.
            if isdone
                break;
            end

        end

        % 4. Create training data. Training is performed using batch data. The
        % batch size equal to the length of the episode.
        batchSize = min(stepCt,maxStepsPerEpisode);
        observationBatch = observationBuffer(:,:,1:batchSize);
        actionBatch = actionBuffer(:,:,1:batchSize);
        rewardBatch = rewardBuffer(:,1:batchSize);

        % Compute the discounted future reward.
        discountedReturn = zeros(1,batchSize);
        for t = 1:batchSize
            G = 0;
            for k = t:batchSize
                G = G + discountFactor ^ (k-t) * rewardBatch(k);
            end
            discountedReturn(t) = G;
        end

        % 5. Organize data to pass to the loss function.
        lossData.batchSize = batchSize;
        lossData.actInfo = actInfo;
        lossData.actionBatch = actionBatch;
        lossData.discountedReturn = discountedReturn;

        % 6. Compute the gradient of the loss with respect to the policy
        % parameters.
        actorGradient = gradient(actor,@actorLossFunction,...
            {observationBatch},lossData);

        % 7. Update the actor network using the computed gradients.
        [actor,actorOptimizer] = update(actorOptimizer,actor,actorGradient);

        % 8. Update the training visualization.
        episodeCumulativeReward = sum(episodeReward);
        episodeCumulativeRewardVector = cat(2,...
            episodeCumulativeRewardVector,episodeCumulativeReward);
        movingAveReward = movmean(episodeCumulativeRewardVector,...
            aveWindowSize,2);
        addpoints(lineReward,episodeCt,episodeCumulativeReward);
        addpoints(lineAveReward,episodeCt,movingAveReward(end));
        drawnow;

        % 9. Terminate training if the network is sufficiently trained.
        if max(movingAveReward) > trainingTerminationValue
            break
        end

    end
else
    load("CustomActor.mat");
end

Simulation

After training, simulate the trained policy.

Before simulation, reset the environment.

rng(0)
obs = reset(env);

Enable the environment visualization, which is updated each time the environment step function is called.

plot(env)

For each simulation step, perform the following actions.

  1. Get the action by sampling from the policy using the getAction function.

  2. Step the environment using the obtained action value.

  3. Terminate if a terminal condition is reached.

for stepCt = 1:maxStepsPerEpisode
    
    % Select action according to trained policy
    action = getAction(actor,{obs});
        
    % Step the environment
    [nextObs,reward,isdone] = step(env,action{1});
    
    % Check for terminal condition
    if isdone
        break
    end
    
    obs = nextObs;
    
end

Functions for Custom Training

To obtain actions and value functions for given observations from Reinforcement Learning Toolbox policy and value function approximators, you can use the following functions.

  • getValue — Obtain the estimated state value or state-action value function.

  • getAction — Obtain the action from an actor based on the current observation.

  • getMaxQValue — Obtain the estimated maximum state-action value function for a discrete Q-value approximator.

If your policy or value function approximator is a recurrent neural network, that is, a neural network with at least one layer that has hidden state information, the preceding functions can return the current network state. You can use the following function syntaxes to get and set the state of your approximator.

  • state = getState(critic) — Obtain the state of approximator critic.

  • newCritic = setState(oldCritic,state) — Set the state of approximator newCritic, and return the result in oldCritic.

  • newCritic = resetState(oldCritic) — Reset all state values of oldCritic to zero and return the result in newCritic.

You can get and set the learnable parameters of your approximator using the getLearnableParameters and setLearnableParameters function, respectively.

In addition to these functions, you can use the setLoss, gradient, optimize, and syncParameters functions to set parameters and compute gradients for your policy and value function approximators.

gradient

The gradient function computes the gradients of the approximator loss function. You can compute several different gradients. For example, to compute the gradient of the approximator outputs with respect to its inputs, use the following syntax.

grad = gradient(actor,"output-input",inputData)

Here:

  • actor is a policy or value function approximator object.

  • inputData contains values for the input channels to the approximator.

  • grad contains the computed gradients.

For more information, at the MATLAB command line, type help rl.function.AbstractFunction.gradient

syncParameters

The syncParameters function updates the learnable parameters of one policy or value function approximator based on those of another approximator. This function is useful for updating a target actor or critic approximator, as is done for DDPG agents. To synchronize parameters values between two approximators, use the following syntax.

newTargetApproximator = syncParameters(oldTargetApproximator,sourceApproximator,smoothFactor)

Here:

  • oldTargetApproximator is a policy or value function approximator object with parameters θold.

  • sourceApproximator is a policy or value function approximator object with the same structure as oldTargetRep, but with parameters θsource.

  • smoothFactor is a smoothing factor (τ) for the update.

  • newTargetApproximator has the same structure as oldRep, but its parameters are θnew=τθsource+(1-τ)θold.

Loss Function

The loss function in the REINFORCE algorithm is the product of the discounted reward and the log of the policy, summed across all time steps. The discounted reward calculated in the custom training loop must be resized to make it compatible for multiplication with the policy.

function loss = actorLossFunction(policy, lossData)

    policy = policy{1};

    % Create the action indication matrix.
    batchSize = lossData.batchSize;
    Z = repmat(lossData.actInfo.Elements',1,batchSize);
    actionIndicationMatrix = lossData.actionBatch(:,:) == Z;
    
    % Resize the discounted return to the size of policy.
    G = actionIndicationMatrix .* lossData.discountedReturn;
    G = reshape(G,size(policy));
    
    % Round any policy values less than eps to eps.
    policy(policy < eps) = eps;
    
    % Compute the loss.
    loss = -sum(G .* log(policy),'all');
    
end

Helper Function

The following helper function creates a figure for training visualization.

function [trainingPlot, lineReward, lineAveReward] = hBuildFigure()
    plotRatio = 16/9;
    trainingPlot = figure(...
                'Visible','off',...
                'HandleVisibility','off', ...
                'NumberTitle','off',...
                'Name','Cart Pole Custom Training');
    trainingPlot.Position(3) = plotRatio * trainingPlot.Position(4);
    
    ax = gca(trainingPlot);
    
    lineReward = animatedline(ax);
    lineAveReward = animatedline(ax,'Color','r','LineWidth',3);
    xlabel(ax,'Episode');
    ylabel(ax,'Reward');
    legend(ax,'Cumulative Reward','Average Reward','Location','northwest')
    title(ax,'Training Progress');
end

See Also

Related Topics