Agents
A reinforcement learning agent receives observations and a reward from the environment, and returns an action to the environment. During training, the agent continuously updates its parameters to improve its policy for the given environment.
Reinforcement Learning Toolbox™ software provides built-in reinforcement learning agents that use several common algorithms, such as Q-Learning, DQN, PG, AC, DDPG, TD3, SAC and PPO. You can also implement your own custom agents.
For an introduction to agents, see Reinforcement Learning Agents. For an introduction to policies, value functions, actors and critics, see Create Policies and Value Functions.
Apps
Reinforcement Learning Designer | Design, train, and simulate reinforcement learning agents (Since R2021a) |
Blocks
RL Agent | Reinforcement learning agent |
Functions
Topics
Agent Basics
- Reinforcement Learning Agents
You can create an agent using one of several standard reinforcement learning algorithms or define your own custom agent. - Create Agents Using Reinforcement Learning Designer
Interactively create or import agents for training using the Reinforcement Learning Designer app.
Agent Types
- Q-Learning Agents
Create Q-learning agents for reinforcement learning. - SARSA Agents
Create SARSA agents for reinforcement learning. - Deep Q-Network (DQN) Agents
Create DQN agents for reinforcement learning. - Policy Gradient (PG) Agents
Create policy gradient agents for reinforcement learning. - Actor-Critic (AC) Agents
Create actor-critic agents for reinforcement learning. - Proximal Policy Optimization (PPO) Agents
Create PPO agents for reinforcement learning. - Trust Region Policy Optimization (TRPO) Agents
Create TRPO agents for reinforcement learning. - Deep Deterministic Policy Gradient (DDPG) Agents
Create DDPG agents for reinforcement learning. - Twin-Delayed Deep Deterministic (TD3) Policy Gradient Agents
Create TD3 agents for reinforcement learning. - Soft Actor-Critic (SAC) Agents
Create SAC agents for reinforcement learning. - Model-Based Policy Optimization (MBPO) Agents
A model-based (MBPO) reinforcement learning agent learns a model of its environment that it can use to generate additional experiences for training.
Custom Agents
- Create Custom Reinforcement Learning Agents
Create custom agents. - Create and Train Custom PG Agent
Create a custom PG agent and train it using the built-in train function. - Create and Train Custom LQR Agent
Create a custom agent that solves an LQR problem and train it using the built-in train function.