rlSARSAAgent
SARSA reinforcement learning agent
Description
The SARSA algorithm is an on-policy reinforcement learning method for environments with a discrete action space. A SARSA agent trains a Q-value function critic to estimate the value of the current epsilon-greedy policy (it does not try to directly learn an optimal policy).
Note
SARSA agents do not support recurrent networks.
For more information on SARSA agents, see SARSA Agent.
For more information on the different types of reinforcement learning agents, see Reinforcement Learning Agents.
Creation
Description
creates a SARSA agent with the specified critic network and sets the agent
= rlSARSAAgent(critic
,agentOptions
)AgentOptions
property.
Input Arguments
Properties
Object Functions
train | Train reinforcement learning agents within a specified environment |
sim | Simulate trained reinforcement learning agents within specified environment |
getAction | Obtain action from agent, actor, or policy object given environment observations |
getActor | Extract actor from reinforcement learning agent |
setActor | Set actor of reinforcement learning agent |
getCritic | Extract critic from reinforcement learning agent |
setCritic | Set critic of reinforcement learning agent |
generatePolicyFunction | Generate MATLAB function that evaluates policy of an agent or policy object |
Examples
Version History
Introduced in R2019a
See Also
Apps
Functions
getAction
|getActor
|getCritic
|getModel
|generatePolicyFunction
|generatePolicyBlock
|getActionInfo
|getObservationInfo
Objects
rlSARSAAgentOptions
|rlAgentInitializationOptions
|rlVectorQValueFunction
|rlQValueFunction
|rlQAgent
|rlDQNAgent