#Actor-Critic

Articles about Actor-Critic — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with actor-critic. ← All posts

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Pratik Dhanave · ·5 min read

Policy Gradients and Actor-Critic — Learning the Policy Directly

Value-based methods like Q-learning learn how good each action is, then act greedily. There's a different and often better approach: skip the value estimates and learn the policy itself, directly adjusting the probability of actions to get more reward. Policy gradient methods are how RL handles continuous control and stochastic strategies — and the actor-critic architecture that combines them with value learning is the foundation of the algorithms used to fine-tune today's language models.

Instead of learning values then acting greedily, learn the policy directly: adjust action probabilities to earn more reward. Why this handles continuous and stochastic policies, the high-variance problem, and actor-critic (and PPO) — the algorithm family behind RLHF and LLM alignment.

#Reinforcement Learning#Policy Gradient#Actor-Critic#PPO#Machine Learning#AI Engineering#LLMs

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.