Summary of Bamdp Shaping: a Unified Theoretical Framework For Intrinsic Motivation and Reward Shaping, by Aly Lidayan et al.
BAMDP Shaping: a Unified Theoretical Framework for Intrinsic Motivation and Reward Shaping
by Aly Lidayan, Michael Dennis, Stuart Russell
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A new framework for understanding reward shaping in reinforcement learning (RL) agents is introduced, which formalizes the value of exploration by formulating the RL process as updating a prior over possible Markov Decision Processes (MDPs) through experience. This characterization views RL algorithms as BAMDP policies and demonstrates how pseudo-rewards can guide suboptimal algorithms by compensating for misestimation of state values. The paper shows that when pseudo-rewards are Bayes-Adaptive MDP Potential-based Shaping Functions (BAMPFs), they preserve optimal behavior; otherwise, they can corrupt even optimal learners. The authors provide guidance on designing or converting existing pseudo-rewards to BAMPFs by expressing assumptions about the environment as potential functions on BAMDP states. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reward shaping is a way to make reinforcement learning (RL) agents learn faster and better. But sometimes, these “helpful” rewards can actually hurt performance. A new approach, called Bayes-Adaptive Markov Decision Processes (BAMDPs), helps us understand how to use rewards in RL. By looking at the value of information collected during learning, we can see how pseudo-rewards can help or harm an agent’s performance. The authors show that some pseudo-rewards are good and some are bad, and they give tips on how to design better rewards. |
Keywords
» Artificial intelligence » Reinforcement learning
