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Agents

Create and configure reinforcement learning agents using common algorithms, such as SARSA, DQN, DDPG, and A2C

A reinforcement learning agent receives observations and a reward from the environment. Using its policy, the agent selects an action based on the observations and reward, and sends the action to the environment. During training, the agent continuously updates the policy parameters based on the action, observations, and reward. Doing so, allows the agent to learn the optimal policy for the given environment and reward signal.

Reinforcement Learning Toolbox™ software provides reinforcement learning agents that use several common algorithms, such as SARSA, DQN, DDPG, and A2C. You can also implement other agent algorithms by creating your own custom agents. For more information, see Reinforcement Learning Agents.

For more information on defining policy representations, see Create Policy and Value Function Representations.

Functions

expand all

rlQAgentCreate Q-learning reinforcement learning agent
rlSARSAAgentCreate SARSA reinforcement learning agent
rlDQNAgentCreate deep Q-network reinforcement learning agent
rlDDPGAgentCreate deep deterministic policy gradient reinforcement learning agent
rlPGAgentCreate policy gradient reinforcement learning agent
rlACAgentCreate actor-critic reinforcement learning agent
rlQAgentOptionsCreate options for Q-learning agent
rlSARSAAgentOptionsCreate options for SARSA agent
rlDQNAgentOptionsCreate options for DQN agent
rlDDPGAgentOptionsCreate options for DDPG agent
rlPGAgentOptionsCreate options for PG agent
rlACAgentOptionsCreate options for AC agent
getActorGet actor representation from reinforcement learning agent
getCriticGet critic representation from reinforcement learning agent
setActorSet actor representation of reinforcement learning agent
setCriticSet critic representation of reinforcement learning agent

Topics

Reinforcement Learning Agents

You can create an agent using one of several standard reinforcement learning algorithms or define your own custom agent.

Q-Learning Agents

Create Q-learning agents for reinforcement learning.

SARSA Agents

Create SARSA agents for reinforcement learning.

Deep Q-Network Agents

Create DQN agents for reinforcement learning.

Deep Deterministic Policy Gradient Agents

Create DDPG agents for reinforcement learning.

Policy Gradient Agents

Create PG agents for reinforcement learning.

Actor-Critic Agents

Create AC agents for reinforcement learning.

Custom Agents

Create agents that use custom reinforcement learning algorithms.