Soft actor-critic reinforcement learning agent
The soft actor-critic (SAC) algorithm is a model-free, online, off-policy, actor-critic reinforcement learning method. The SAC algorithm computes an optimal policy that maximizes both the long-term expected reward and the entropy of the policy. The policy entropy is a measure of policy uncertainty given the state. A higher entropy value promotes more exploration. Maximizing both the reward and the entropy balances exploration and exploitation of the environment.
For more information, see Soft Actor-Critic Agents.
For more information on the different types of reinforcement learning agents, see Reinforcement Learning Agents.
creates a SAC agent for an environment with the given observation and action
specifications. (agent
= rlSACAgent(observationInfo
,actionInfo
)observationInfo
) and action specifications
(actionInfo
). The actor and critic representations in the agent
use default deep neural networks built using the observation specification
observationInfo
and action specification
actionInfo
.
creates a SAC agent with deep neural network representations configured using the
specified initialization options (agent
= rlSACAgent(observationInfo
,actionInfo
,initOptions
)initOptions
).
sets the agent
= rlSACAgent(___,agentOptions
)AgentOptions
property for any of the previous syntaxes.
train | Train reinforcement learning agents within a specified environment |
sim | Simulate trained reinforcement learning agents within specified environment |
getAction | Obtain action from agent or actor representation given environment observations |
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 |
Deep Network Designer | rlAgentInitializationOptions
| rlSACAgentOptions
| rlStochasticActorRepresentation
| rlValueRepresentation