Train PG Agent to Balance Discrete Cart-Pole System

Fix Random Seed Generator to Improve Reproducibility

The example code may involve computation of random numbers at various stages such as initialization of the agent, creation of the actor and critic, resetting the environment during simulations, initializing the environment state, generating observations (for stochastic environments), generating exploration actions, and sampling min-batches of experiences for learning. Fixing the random number stream preserves the sequence of the random numbers every time you run the code and improves reproducibility of results. You will fix the random number stream at various locations in the example.

Fix the random number stream with the seed 0 and random number algorithm Mersenne Twister. For more information on random number generation see rng.

previousRngState = rng(0,"twister");

The output previousRngState is a structure that contains information about the previous state of the stream. You will restore the state at the end of the example.

Discrete Action Space Cart-Pole MATLAB Environment

The reinforcement learning environment for this example is a pole attached to an unactuated joint on a cart, which moves along a frictionless track. The training goal is to make the pendulum stand upright without falling over.

For this environment:

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

Create Environment Object

Create a predefined environment interface for the pendulum.

env = rlPredefinedEnv("CartPole-Discrete")

env = 
  CartPoleDiscreteAction with properties:

                  Gravity: 9.8000
                 MassCart: 1
                 MassPole: 0.1000
                   Length: 0.5000
                 MaxForce: 10
                       Ts: 0.0200
    ThetaThresholdRadians: 0.2094
               XThreshold: 2.4000
      RewardForNotFalling: 1
        PenaltyForFalling: -5
                    State: [4x1 double]

The interface has a discrete action space where the agent can apply one of two possible force values to the cart, –10 or 10 N.

Obtain the observation and action information from the environment interface.

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

Create PG Agent with Custom Networks

For policy gradient agents, the actor executes a stochastic policy, which for discrete action spaces is approximated by a discrete categorical actor. This actor must take the observation signal as input and return a probability for each action.

As a simple parametrized policy is all you need to stabilize the pole on the cart, define a custom PG agent without any baseline. To implement the parametrized policy within the actor, use a simple neural network with just one hidden layer containing 10 neurons.

Define the network as an array of layer objects, and get the dimension of the observation space and the number of possible actions from the environment specification objects. When you create the network, the initial parameters are initialized with random values. Fix the random number stream so that the agent is always initialized with the same parameter values.

rng(0,"twister");
actorNet = [
    featureInputLayer(prod(obsInfo.Dimension))
    fullyConnectedLayer(10)
    reluLayer
    fullyConnectedLayer(numel(actInfo.Elements))
    softmaxLayer
    ];

For more information on creating a deep neural network policy representation, see Create Policies and Value Functions.

Convert to dlnetwork and display the number of weights.

actorNet = dlnetwork(actorNet);
summary(actorNet)

   Initialized: true

   Number of learnables: 72

   Inputs:
      1   'input'   4 features

Create the actor representation using the specified deep neural network and the environment specification objects. For more information, see rlDiscreteCategoricalActor.

actor = rlDiscreteCategoricalActor(actorNet,obsInfo,actInfo);

To return the probability distribution of the possible actions as a function of a random observation, and given the current network weights, use evaluate.

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

ans = 2x1 single column vector

    0.7229
    0.2771

Create the agent using the actor. For more information, see rlPGAgent.

agent = rlPGAgent(actor);

Check the agent with a random observation input.

getAction(agent,{rand(obsInfo.Dimension)})

ans = 1x1 cell array
    {[-10]}

Specify training options for the actor. Alternatively, you can use rlPGAgentOptions and rlOptimizerOptions objects.

For this training:

agent.AgentOptions.ActorOptimizerOptions = ...
    rlOptimizerOptions(LearnRate=5e-3, ...
    GradientThreshold=1);

Train Agent

To train the agent, first specify the training options. For this example, use the following options.

For more information, see rlTrainingOptions.

% training options
trainOpts = rlTrainingOptions(...
    MaxEpisodes=1000, ...
    MaxStepsPerEpisode=500, ...
    Verbose=false, ...
    Plots="training-progress",...
    StopTrainingCriteria="EvaluationStatistic",...
    StopTrainingValue=500);

% agent evaluator
evl = rlEvaluator(EvaluationFrequency=20, NumEpisodes=10);

Fix the random stream for reproducibility.

rng(0,"twister");

Train the agent using the train function. Training this agent is a computationally intensive process that takes several minutes to complete. To save time while running this example, load a pretrained agent by setting doTraining to false. To train the agent yourself, set doTraining to true.

doTraining = false;
if doTraining
    % Train the agent.
    trainingStats = train(agent,env,trainOpts,Evaluator=evl);
else
    % Load the pretrained agent for the example.
    load("MATLABCartpolePG.mat","agent");
end

A snapshot of the training is shown below. You may receive different results due to randomness in the training process.

Simulate PG Agent

Fix the random stream for reproducibility.

rng(0,"twister");

You can visualize the cart-pole system by using the plot function.

plot(env)

Use a deterministic policy for simulation.

agent.UseExplorationPolicy = false;

To validate the performance of the trained agent, simulate it within the cart-pole environment. For more information on agent simulation, see rlSimulationOptions and sim.

simOptions = rlSimulationOptions(MaxSteps=500);
experience = sim(env,agent,simOptions);

The agent can balance the cart-pole system. Display the total reward obtained during the simulation.

totalReward = sum(experience.Reward)

totalReward = 
500

Restore the random number stream using the information stored in previousRngState.

rng(previousRngState);