# 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.

`rlValueFunction`

- State value function approximator`rlQValueFunction`

- State-action value function approximator with scalar output`rlVectorQValueFunction`

- State-action function approximator with vector output`rlContinuousDeterministicActor`

- Continuous deterministic actor`rlDiscreteCategoricalActor`

- Discrete stochastic actor`rlContinuousGaussianActor`

- Continuous Gaussian actor (stochastic)

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 (PG) 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 dimension of the observation space (`numObs`

) and the number of possible 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 modeled 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) fullyConnectedLayer(24) reluLayer fullyConnectedLayer(24) reluLayer fullyConnectedLayer(2) softmaxLayer ];

Convert to `dlnetwork`

.

actorNetwork = dlnetwork(actorNetwork);

Create the actor using an `rlDiscreteCategoricalActor`

object.

actor = rlDiscreteCategoricalActor(actorNetwork,obsInfo,actInfo);

Accelerate the gradient computation of the actor.

actor = accelerate(actor,true);

Evaluate the policy with a random observation as input.

policyEvalOutCell = evaluate(actor,{rand(obsInfo.Dimension)}); policyEvalOut = policyEvalOutCell{1}

`policyEvalOut = `*2x1 single column vector*
0.4682
0.5318

Create the optimizer using `rlOptimizer`

and `rlOptimizerOptions`

function.

actorOpts = rlOptimizerOptions(LearnRate=1e-2); 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; avgWindowSize = 100; trainingTerminationValue = 220;

Create a vector to store the cumulative reward for each training episode.

episodeCumulativeRewardVector = [];

Create a figure for training visualization using the `hBuildFigure`

helper function.

[trainingPlot,lineReward,lineAveReward] = hBuildFigure;

### Custom Training Loop

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

Reset the environment.

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

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.

Collect the training data as a batch of experiences.

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

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

Update the policy using the computed gradients.

Update the training visualization.

Terminate training if the policy is sufficiently trained.

% 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 the 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}); % Apply the action to the environment % and obtain the resulting observation and reward. [nextObs,reward,isdone] = step(env,action{1}); % Store the action, observation, % and reward experiences in their buffers. observationBuffer(:,:,stepCt) = obs; actionBuffer(:,:,stepCt) = action{1}; 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 cannot exceed 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. % for more information, at the command line, type: % help rl.optimizer.AbstractOptimizer/update [actor,actorOptimizer] = update( ... actorOptimizer, ... actor, ... actorGradient); % 8. Update the training visualization. episodeCumulativeReward = sum(episodeReward); episodeCumulativeRewardVector = cat(2,... episodeCumulativeRewardVector,episodeCumulativeReward); movingAvgReward = movmean(episodeCumulativeRewardVector,... avgWindowSize,2); addpoints(lineReward,episodeCt,episodeCumulativeReward); addpoints(lineAveReward,episodeCt,movingAvgReward(end)); drawnow; % 9. Terminate training if the network is sufficiently trained. if max(movingAvgReward) > trainingTerminationValue break end end

### Simulation

After training, simulate the trained policy.

Before simulation, reset the environment.

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.

Get the action by sampling from the policy using the

`getAction`

function.Step the environment using the obtained action value.

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 `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 sum 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 (e.g. an observation).`grad`

contains the computed gradients.

For more information, see `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 ${\theta}_{\mathrm{old}}$.`sourceApproximator`

is a policy or value function approximator object with the same structure as`oldTargetRep`

, but with parameters ${\theta}_{\mathrm{source}}$.`smoothFactor`

is a smoothing factor ($\tau $) for the update.`newTargetApproximator`

has the same structure as`oldRep`

, but its parameters are ${\theta}_{\mathrm{new}}=\tau {\theta}_{\mathrm{source}}+\left(1-\tau \right){\theta}_{\mathrm{old}}$.

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

### Loss Function

The loss function in the REINFORCE algorithm the product between the discounted reward and the logarithm of the probability distribution of the action (coming from the policy evaluation for a given observation), summed across all time steps. The discounted reward calculated in the custom training loop must be resized to be multiplied with the logarithm of the action probability distribution.

The function first input parameter must be a cell array like the one returned from the evaluation of a function approximator object. For more information, see the description of `outData`

in `evaluate`

. The second, optional, input argument contains additional data that might be needed by the loss calculation function. For more information, see `gradient`

.

function loss = actorLossFunction(ActProbCell,lossFcnStruct) % Extract the matrix resulting from the policy evaluation ActProb = ActProbCell{1}; % Create the action indication matrix. batchSize = lossFcnStruct.batchSize; Z = repmat(lossFcnStruct.actInfo.Elements',1,batchSize); actionIndicationMatrix = (lossFcnStruct.actionBatch(:,:)==Z); % Resize the discounted return to the size of ActProb. G = actionIndicationMatrix .* lossFcnStruct.discountedReturn; G = reshape(G,size(ActProb)); % Round any action probability values less than eps to eps. ActProb(ActProb < eps) = eps; % Compute the loss. loss = -sum(G .* log(ActProb),"all"); end

### Helper Function

The following helper function creates a figure for training visualization.

function [trainingPlt, lineRewd, lineAvgRwd] = hBuildFigure() plotRatio = 16/9; trainingPlt = figure(... Visible="off",... HandleVisibility="off", ... NumberTitle="off",... Name="Cart Pole Custom Training"); trainingPlt.Position(3) = ... plotRatio * trainingPlt.Position(4); ax = gca(trainingPlt); lineRewd = animatedline(ax); lineAvgRwd = 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

### Functions

### Objects

## Related Examples

- Custom Training Loop with Simulink Action Noise
- Train Custom LQR Agent
- Create Agent for Custom Reinforcement Learning Algorithm