Simulate a reinforcement learning environment with an agent configured for that environment. For this example, load an environment and agent that are already configured. The environment is a discrete cart-pole environment created with rlPredefinedEnv. The agent is a policy gradient (rlPGAgent) agent. For more information about the environment and agent used in this example, see Train PG Agent to Balance Cart-Pole System.

rng(0) % for reproducibility
load RLSimExample.mat
env

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]

agent

agent = 
  rlPGAgent with properties:

            AgentOptions: [1×1 rl.option.rlPGAgentOptions]
    UseExplorationPolicy: 1
         ObservationInfo: [1×1 rl.util.rlNumericSpec]
              ActionInfo: [1×1 rl.util.rlFiniteSetSpec]
              SampleTime: 0.1000

Typically, you train the agent using train and simulate the environment to test the performance of the trained agent. For this example, simulate the environment using the agent you loaded. Configure simulation options, specifying that the simulation run for 100 steps.

simOpts = rlSimulationOptions('MaxSteps',100);

For the predefined cart-pole environment used in this example. you can use plot to generate a visualization of the cart-pole system. When you simulate the environment, this plot updates automatically so that you can watch the system evolve during the simulation.

plot(env)

Simulate the environment.

experience = sim(env,agent,simOpts)

experience = struct with fields:
       Observation: [1×1 struct]
            Action: [1×1 struct]
            Reward: [1×1 timeseries]
            IsDone: [1×1 timeseries]
    SimulationInfo: [1×1 struct]

The output structure experience records the observations collected from the environment, the action and reward, and other data collected during the simulation. Each field contains a timeseries object or a structure of timeseries data objects. For instance, experience.Action is a timeseries containing the action imposed on the cart-pole system by the agent at each step of the simulation.

experience.Action

ans = struct with fields:
    CartPoleAction: [1×1 timeseries]

Simulate an environment created for the Simulink® model used in the example Train Multiple Agents to Perform Collaborative Task, using the agents trained in that example.

Load the agents in the MATLAB® workspace.

load rlCollaborativeTaskAgents

Create an environment for the rlCollaborativeTask Simulink® model, which has two agent blocks. Since the agents used by the two blocks (agentA and agentB) are already in the workspace, you do not need to pass their observation and action specifications to create the environment.

env = rlSimulinkEnv('rlCollaborativeTask',["rlCollaborativeTask/Agent A","rlCollaborativeTask/Agent B"]);

Load the parameters that are needed by the rlCollaborativeTask Simulink® model to run.

rlCollaborativeTaskParams

Simulate the agents against the environment, saving the experiences in xpr.

xpr = sim(env,[agentA agentB]);

Plot actions of both agents.

subplot(2,1,1); plot(xpr(1).Action.forces)
subplot(2,1,2); plot(xpr(2).Action.forces)