rlQAgent
Q-learning reinforcement learning agent
Description
The Q-learning algorithm is a model-free, online, off-policy reinforcement learning method. A Q-learning agent is a value-based reinforcement learning agent which trains a critic to estimate the return or future rewards.
For more information on Q-learning agents, see Q-Learning Agents.
For more information on the different types of reinforcement learning agents, see Reinforcement Learning Agents.
Creation
Description
creates a Q-learning agent with the specified critic network and sets the agent
= rlQAgent(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 | Get actor from reinforcement learning agent |
setActor | Set actor of reinforcement learning agent |
getCritic | Get critic from reinforcement learning agent |
setCritic | Set critic of reinforcement learning agent |
generatePolicyFunction | Generate function that evaluates policy of an agent or policy object |
Examples
Version History
Introduced in R2019a