Policy Gradient
IntermediateOptimizing policies directly via gradient ascent on expected reward.
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Why It Matters
Policy gradient methods are essential in reinforcement learning as they enable the direct optimization of complex policies, making them suitable for a wide range of applications, including robotics, game playing, and natural language processing. Their ability to handle high-dimensional action spaces and stochastic environments has made them a cornerstone of modern AI systems.
Policy gradient methods are a class of reinforcement learning algorithms that optimize the policy directly by maximizing the expected return using gradient ascent techniques. Unlike value-based methods, which derive policies from value functions, policy gradient approaches parameterize the policy as a function π(a|s; θ), where θ represents the parameters of the policy. The objective is to maximize the expected return J(θ) = E[Σ_t γ^t R_t], where R_t is the reward at time t and γ is the discount factor. The policy gradient theorem provides a way to compute the gradient of the expected return with respect to the policy parameters, given by ∇J(θ) = E[∇ log π(a|s; θ) Q(s, a)], where Q(s, a) is the action-value function. This approach allows for the optimization of stochastic policies and is particularly useful in high-dimensional action spaces and environments with continuous actions. Notable algorithms utilizing policy gradients include REINFORCE and Proximal Policy Optimization (PPO).