Description

Policy Gradient methods are a class of reinforcement learning algorithms that optimize policies directly without necessarily learning a value function. These methods work by estimating the gradient of the expected return with respect to the policy parameters and then updating the parameters in the direction of the gradient. Policy gradient methods are particularly useful in high-dimensional or continuous action spaces where value-based methods might struggle.

Examples

• 🔄 REINFORCE algorithm
• 🎭 Actor-Critic methods
• 🔁 Proximal Policy Optimization (PPO)

Applications