Policy Gradients: REINFORCE and Actor-Critic

2026-06-06 · Source: Daily Dose of Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Advanced, short

Summary

Reinforcement learning (RL) methods typically learn a value function to derive optimal actions, a strategy effective for discrete and limited action spaces. However, for continuous or numerous actions, policy gradient methods offer an alternative by directly parameterizing and optimizing the policy itself. This approach involves learning policy parameters, denoted as θ, to maximize the expected return J(θ) over trajectories τ. A key challenge is computing the gradient of J(θ) when the trajectory distribution depends on θ. The "log-derivative trick" provides an elegant solution, transforming the gradient of a probability into the probability multiplied by the gradient of a log-probability, enabling estimation via sampling. This technique is fundamental to methods like REINFORCE and actor-critic architectures, which aim to adjust policy weights for improved behavior.

Key takeaway

For Machine Learning Engineers designing agents for environments with continuous or high-dimensional action spaces, understanding policy gradient methods is crucial. You should consider directly parameterizing your policy and optimizing its parameters via gradient ascent on expected return, rather than relying solely on value-based methods. This approach, enabled by the log-derivative trick, offers a robust alternative when traditional Q-learning struggles with action complexity.

Key insights

Policy gradient methods directly optimize a parameterized policy to maximize expected return, suitable for continuous action spaces.

Principles

• Direct policy parameterization avoids value function derivation.
• Maximize expected return J(θ) via gradient ascent.
• Log-derivative trick enables gradient computation for expectations.

Method

Parameterize policy πθ(a|s) directly; define objective J(θ) as expected return R(τ) over trajectories τ. Compute ∇θJ(θ) using the log-derivative trick to perform gradient ascent on θ.

Topics

• Policy Gradients
• Reinforcement Learning
• REINFORCE Algorithm
• Actor-Critic
• Continuous Control
• Log-derivative Trick

Best for: AI Scientist, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Daily Dose of Data Science.