Reinforcement learning (RL) with neural networks is a type of machine learning where an agent learns to make decisions based on feedback from its environment. RL with neural networks has been applied to a wide range of applications, including game playing, robotics, and autonomous driving.
In RL, an agent interacts with an environment by taking actions and receiving rewards or penalties. The goal of the agent is to learn a policy, or a mapping from states to actions, that maximizes its expected cumulative reward over time. RL with neural networks typically involves the use of a neural network as a function approximator to estimate the value function or policy.
There are two main types of RL with neural networks: value-based methods and policy-based methods. Value-based methods use a neural network to estimate the value function, which represents the expected cumulative reward for each state-action pair. The agent then selects actions based on the value estimates. Examples of value-based methods include Q-learning and Deep Q-Networks (DQNs).
Policy-based methods use a neural network to directly learn a policy that maps states to actions. The neural network takes the current state as input and outputs a probability distribution over the possible actions. The agent then selects actions based on the probabilities generated by the policy. Examples of policy-based methods include Policy Gradients and Actor-Critic methods.
RL with neural networks can be challenging to train, as the agent must balance exploration, or trying out new actions, with exploitation, or choosing the actions that are most likely to lead to high rewards. Additionally, the feedback from the environment can be delayed or sparse, making it difficult for the agent to learn a good policy. Techniques such as experience replay and target networks have been developed to improve the stability and convergence of RL with neural networks.