What is the difference between value-based and policy-based reinforcement learning?

Reinforcement learning (RL) is a type of machine learning where an agent learns to take actions in an environment to maximize a reward signal. There are two main approaches to RL: value-based and policy-based.

Value-based RL algorithms attempt to learn an optimal value function that estimates the expected cumulative reward for each state or state-action pair. Q-learning and Deep Q-Networks (DQNs) are examples of value-based RL algorithms. These algorithms learn a value function that is used to derive an optimal policy for the agent to follow.

Policy-based RL algorithms, on the other hand, learn a policy directly. The goal is to optimize the policy to maximize the expected cumulative reward. Policy Gradient algorithms are examples of policy-based RL algorithms. These algorithms learn a policy that maps states to actions directly.

The main difference between the two approaches is the way the optimal policy is derived. Value-based RL algorithms use the learned value function to derive an optimal policy, while policy-based RL algorithms directly optimize the policy. Policy-based algorithms can handle continuous action spaces better than value-based algorithms, but they are generally less sample efficient than value-based algorithms.