Advanced Policies: A First-Principles Path from Policy Gradient to Q-Learning

arXiv:2005.08844 · cs.LG, stat.ML · Submitted 2020-05-18 · Read on arXiv

cs.LG, stat.ML

Submitted: 2020-05-18

Updated: 2026-09-27

Code: https://github.com/hill-a/stable-baselines

Terminology

• Objective Function — 22× in this paper · explained in 2 episodes
• Q-learning — 20× in this paper · explained in 3 episodes
• Actor — 18× in this paper · explained in 3 episodes
• Reinforcement Learning — 11× in this paper · explained in 4 episodes
• Action Space — 9× in this paper · explained in 1 episode
• Regularization — 7× in this paper · explained in 3 episodes
• Advanced Actor Critic — 3× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Fisher Information — 1× in this paper · explained in 3 episodes
• Fisher Information Matrix — 1× in this paper
• Reward Function — 1× in this paper · explained in 2 episodes

Sources

• OpenAI Gym
• Addressing Function Approximation Error in Actor-Critic Methods
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
• Bridging the Gap Between Value and Policy Based Reinforcement Learning
• Equivalence Between Policy Gradients and Soft Q-Learning
• Sample Efficient Actor-Critic with Experience Replay

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