Summary of Conservative Ddpg — Pessimistic Rl Without Ensemble, by Nitsan Soffair et al.
Conservative DDPG – Pessimistic RL without Ensemble
by Nitsan Soffair, Shie Mannor
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses a long-standing issue in Deep Deterministic Policy Gradients (DDPG), known as the overestimation bias problem. DDPG’s Q-estimates tend to overstate actual Q-values, hindering its performance. Traditional solutions involve ensemble-based methods or complex log-policy-based approaches, which are computationally expensive and difficult to implement. In contrast, this study proposes a straightforward solution using a Q-target and incorporating a behavioral cloning (BC) loss penalty. This approach acts as an uncertainty measure, requiring minimal code changes and no ensemble formation. The proposed Conservative DDPG outperforms traditional DDPG across various MuJoCo and Bullet tasks, achieving better performance in all evaluated tasks and even competitive or superior results compared to TD3 and TD7, with significantly reduced computational requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in Deep Deterministic Policy Gradients (DDPG) that makes it less accurate. DDPG usually overestimates how well it will do in the future, which is not good. People have tried to fix this by using special methods or complex math, but these solutions are hard to understand and use. Instead, this study suggests a simple way to make DDPG better by adding something called a behavioral cloning (BC) penalty. This helps DDPG be more accurate and makes it work faster on computers. The new method is called Conservative DDPG, and it works well in many different situations. |
