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. The action space can only be continuous.
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, using default initialization options. The actor and critic
representations in the agent use default deep neural networks built using the
observation specification agent
= rlSACAgent(observationInfo
,actionInfo
)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 AgentOptions
property for any of the previous syntaxes.agent
= rlSACAgent(___,agentOptions
)
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