rlSARSAAgent

SARSA reinforcement learning agent

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

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

Input Arguments

critic — Critic network representation

rlQValueRepresentation object

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

Properties

AgentOptions — Agent options

rlSARSAAgentOptions object

Agent options, specified as an rlSARSAAgentOptions object.

Object Functions

train	Train a reinforcement learning agent within a specified environment
sim	Simulate a trained reinforcement learning agent within a specified environment
getActor	Get actor representation from reinforcement learning agent
setActor	Set actor representation of reinforcement learning agent
getCritic	Get critic representation from reinforcement learning agent
setCritic	Set critic representation of reinforcement learning agent
generatePolicyFunction	Create function that evaluates trained policy of reinforcement learning agent

Examples

Create a SARSA Agent

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.