Main Content

Training and Validation

Train and simulate reinforcement learning agents

To learn an optimal policy, a reinforcement learning agent interacts with the environment through a repeated trial-and-error process. During training, the agent tunes the parameters of its policy representation to maximize the long-term reward. Reinforcement Learning Toolbox™ software provides functions for training agents and validating the training results through simulation. For more information, see Train Reinforcement Learning Agents.

Apps

Reinforcement Learning DesignerDesign, train, and simulate reinforcement learning agents

Functions

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trainTrain reinforcement learning agents within a specified environment
rlTrainingOptionsOptions for training reinforcement learning agents
rlMultiAgentTrainingOptionsOptions for training multiple reinforcement learning agents
inspectTrainingResultPlot training information from a previous training session
simSimulate trained reinforcement learning agents within specified environment
rlSimulationOptionsOptions for simulating a reinforcement learning agent within an environment
runEpisodeSimulate reinforcement learning environment against policy or agent
setupSet up reinforcement learning environment to run multiple simulations
cleanupClean up reinforcement learning environment after running multiple simulations

Blocks

RL AgentReinforcement learning agent

Topics

Training and Simulation Basics

Using the Reinforcement Learning Designer App

Using Multiple Processes and GPUs

Train Agents in MATLAB Environments

Train Agents in Simulink Environments

Multi-Agent Training

Generate Rewards from Control Specifications

Imitation Learning

Custom Agents and Training Algorithms