What are Policy Gradients?
Policy gradients are a class of algorithms in reinforcement learning that optimize the policy directly. Unlike value-based methods that estimate the value function, policy gradient methods focus on adjusting the policy parameters to maximize the expected reward.
In reinforcement learning, an agent interacts with an environment to learn a policy, which is a mapping from states to actions. Policy gradient methods use the gradient of the expected reward with respect to the policy parameters to guide the learning process. By performing gradient ascent on the expected reward, these methods can handle high-dimensional action spaces and stochastic policies effectively.
Key Concepts:
- Stochastic Policies: These policies provide a probability distribution over actions, allowing for exploration and more adaptive learning.
- REINFORCE Algorithm: A fundamental policy gradient algorithm that uses Monte Carlo methods to estimate the gradient.
- Advantage Function: Helps reduce variance in the policy gradient estimates during training, leading to more stable learning.
Overall, policy gradients are essential for developing robust reinforcement learning agents capable of solving complex tasks in uncertain environments.