sim

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 withrlPredefinedEnv. The agent is a policy gradient (rlPGAgent) agent. For more information about the environment and agent used in this example, seeTrain PG Agent to Balance Cart-Pole System.

rng(0)% for reproducibilityloadRLSimExample.matenv

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]

agent

agent = rlPGAgent with properties: AgentOptions: [1x1 rl.option.rlPGAgentOptions] UseExplorationPolicy: 1 ObservationInfo: [1x1 rl.util.rlNumericSpec] ActionInfo: [1x1 rl.util.rlFiniteSetSpec] SampleTime: 0.1000

Typically, you train the agent usingtrainand 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 useplotto 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: [1x1 struct] Action: [1x1 struct] Reward: [1x1 timeseries] IsDone: [1x1 timeseries] SimulationInfo: [1x1 struct]

The output structureexperiencerecords the observations collected from the environment, the action and reward, and other data collected during the simulation. Each field contains atimeseriesobject or a structure oftimeseriesdata objects. For instance,experience.Actionis atimeseriescontaining the action imposed on the cart-pole system by the agent at each step of the simulation.

experience.Action

ans =struct with fields:CartPoleAction: [1x1 timeseries]

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

Load the agents in the MATLAB® workspace.

loadrlCollaborativeTaskAgents

Create an environment for therlCollaborativeTaskSimulink® model, which has two agent blocks. Since the agents used by the two blocks (agentAandagentB) 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 therlCollaborativeTaskSimulink® model to run.

rlCollaborativeTaskParams

Simulate the agents against the environment, saving the experiences inxpr.

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

Plot actions of both agents.

次要情节(2,1,1);情节(xpr (1) .Action.forces)次要情节(2,1,2); plot(xpr(2).Action.forces)