Main Content

Reinforcement Learning

Train deep neural network agents by interacting with an unknown dynamic environment

Reinforcement learning is a goal-directed computational approach where an agent learns to perform a task by interacting with an unknown dynamic environment. During training, the learning algorithm updates the agent policy parameters. The goal of the learning algorithm is to find an optimal policy that maximizes the long-term reward received during the task.

Depending on the type of agent, the policy is represented by one or more policy and value function representations. You can implement these representations using deep neural networks. You can then train these networks using Reinforcement Learning Toolbox™ software.

For more information, see Reinforcement Learning Using Deep Neural Networks.