How do policy gradient methods optimize the policy, and what is the significance of the gradient of the expected reward with respect to the policy parameters?
Tuesday, 11 June 2024
by EITCA Academy
Policy gradient methods are a class of algorithms in reinforcement learning that aim to directly optimize the policy, which is a mapping from states to actions, by adjusting the parameters of the policy function in a way that maximizes the expected reward. These methods are distinct from value-based methods, which focus on estimating the value
- Published in Artificial Intelligence, EITC/AI/ARL Advanced Reinforcement Learning, Deep reinforcement learning, Policy gradients and actor critics, Examination review
Tagged under:
Actor-Critic, Artificial Intelligence, Deep Learning, Policy Gradient, REINFORCE, Reinforcement Learning