Train AC Agent to Balance Cart-Pole System

This example shows how to train an actor-critic (AC) agent to balance a cart-pole system modeled in MATLAB®.

For more information on AC agents, see Actor-Critic Agents. For an example on training AC agent using parallel computing, see Train AC Agent to Balance Cart-pole System Using Parallel Computing example.

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:

  • The upward balanced pendulum position is 0 radians, and the downward hanging position is pi radians

  • The pendulum starts upright with an initial angle of +/- 0.05 radians

  • The force action signal from the agent to the environment is from -10 to 10 N

  • The observations from the environment are the position and velocity of the cart, the pendulum angle, and its derivative

  • The episode terminates if the pole is more than 12 degrees from vertical, or the cart moves more than 2.4 m from the original position

  • A reward of +1 is provided for every time step that the pole remains upright. A penalty of -5 is applied when the pendulum falls.

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

Create Environment Interface

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: [4×1 double]

env.PenaltyForFalling = -10;

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);

Fix the random generator seed for reproducibility.


Create AC agent

An AC agent approximates the long-term reward given observations and actions using a critic value function representation. To create the critic, first create a deep neural network with one input (the observation) and one output (the state value). The input size of the critic network is [4 1 1] since the environment has 4 observations. For more information on creating a deep neural network value function representation, see Create Policy and Value Function Representations.

criticNetwork = [
    imageInputLayer([4 1 1],'Normalization','none','Name','state')

Specify options for the critic representation using rlRepresentationOptions.

criticOpts = rlRepresentationOptions('LearnRate',8e-3,'GradientThreshold',1);

Create the critic representation using the specified deep neural network and options. You must also specify the action and observation information for the critic, which you already obtained from the environment interface. For more information, see rlRepresentation.

critic = rlRepresentation(criticNetwork,obsInfo,'Observation',{'state'},criticOpts);

An AC agent decides which action to take given observations using an actor representation. To create the actor, create a deep neural network with one input (the observation) and one output (the action). The output size of the actor network is 2 since the environment has 2 possible actions, -10 and 10.

Construct the actor in a similar manner to the critic.

actorNetwork = [
    imageInputLayer([4 1 1],'Normalization','none','Name','state')

actorOpts = rlRepresentationOptions('LearnRate',8e-3,'GradientThreshold',1);

actor = rlRepresentation(actorNetwork,obsInfo,actInfo,'Observation',{'state'},'Action',{'action'},actorOpts);

To create the AC agent, first specify the AC agent options using rlACAgentOptions.

agentOpts = rlACAgentOptions(...
    'NumStepsToLookAhead',32, ...

Then, create the agent using the specified actor representation and the default agent options. For more information, see rlACAgent.

agent = rlACAgent(actor,critic,agentOpts);

Train Agent

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

  • Run each training episode for at most 1000 episodes, with each episode lasting at most 500 time steps.

  • Display the training progress in the Episode Manager dialog box (set the Plots option) and disable the command line display (set the Verbose option).

  • Stop training when the agent receives an average cumulative reward greater than 480 over 10 consecutive episodes. At this point, the agent can balance the pendulum in the upright position.

For more information, see rlTrainingOptions.

trainOpts = rlTrainingOptions(...
    'MaxEpisodes',1000, ...
    'MaxStepsPerEpisode',500, ...
    'Verbose',false, ...

The cart-pole system can be visualized with using the plot function during training or simulation.


Train the agent using the train function. This 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);
    % Load pretrained agent for the example.

Simulate AC Agent

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);

totalReward = sum(experience.Reward)
totalReward = 500

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See Also

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