Reflective Policy Optimization

arXiv:2406.03678 · cs.LG, cs.AI, stat.ML · Submitted 2024-06-06 · Read on arXiv

cs.LG, cs.AI, stat.ML

Submitted: 2024-06-06

Updated: 2026-09-21

Comments: 20 pages

Code: https://github.com/Edgargan/RPO

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reflective Policy Optimization — 26× in this paper
• Reinforcement Learning — 24× in this paper · explained in 4 episodes
• Deep Reinforcement Learning — 7× in this paper · explained in 2 episodes
• Objective Function — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Action Space — 5× in this paper · explained in 1 episode
• Sample Efficiency — 5× in this paper · explained in 4 episodes
• Actor — 4× in this paper · explained in 3 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Proximal Policy Optimization — 4× in this paper
• Trust Region Policy Optimization — 4× in this paper
• Introspection — 2× in this paper · explained in 2 episodes
• Markov Decision Process — 2× in this paper
• Proximal Policy Optimization (PPO) — 2× in this paper · explained in 4 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Q-learning — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target
• OpenAI Gym
• Proximal Policy Optimization Algorithms
• Policy Optimization Through Approximate Importance Sampling

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