rlSARSAAgent

SARSA reinforcement learning agent

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Description

The SARSA algorithm is a model-free, online, on-policy reinforcement learning method. A SARSA agent is a value-based reinforcement learning agent which trains a critic to estimate the return or future rewards.

For more information on SARSA agents, see SARSA Agents.

For more information on the different types of reinforcement learning agents, see Reinforcement Learning Agents.

Creation

Syntax

agent = rlSARSAAgent(critic,agentOptions)

Description

example

agent = rlSARSAAgent(critic,agentOptions) creates a SARSA agent with the specified critic network and sets the AgentOptions property.

Input Arguments

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Critic network representation, specified as an rlQValueRepresentation object. For more information on creating critic representations, see Create Policy and Value Function Representations.

Properties

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Agent options, specified as an rlSARSAAgentOptions object.

Object Functions

trainTrain a reinforcement learning agent within a specified environment
simSimulate a trained reinforcement learning agent within a specified environment
getActorGet actor representation from reinforcement learning agent
setActorSet actor representation of reinforcement learning agent
getCriticGet critic representation from reinforcement learning agent
setCriticSet critic representation of reinforcement learning agent
generatePolicyFunctionCreate function that evaluates trained policy of reinforcement learning agent

Examples

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Open Live Script

Create or load an environment interface. For this example load the Basic Grid World environment interface.

env = rlPredefinedEnv("BasicGridWorld");

Create a critic value function representation using a Q table derived from the environment observation and action specifications.

qTable = rlTable(getObservationInfo(env),getActionInfo(env));
critic = rlQValueRepresentation(qTable,getObservationInfo(env),getActionInfo(env));

Create a SARSA agent using the specified critic value function and an epsilon value of 0.05.

opt = rlSARSAAgentOptions;
opt.EpsilonGreedyExploration.Epsilon = 0.05;

agent = rlSARSAAgent(critic,opt)
agent = 
  rlSARSAAgent with properties:

    AgentOptions: [1x1 rl.option.rlSARSAAgentOptions]

To check your agent, use getAction to return the action from a random observation.

getAction(agent,{randi(25)})
ans = 1

You can now test and train the agent against the environment.

See Also

Topics

Introduced in R2019a

Reinforcement Learning Toolbox Documentation
Support