Summary of Bcr-drl: Behavior- and Context-aware Reward For Deep Reinforcement Learning in Human-ai Coordination, by Xin Hao et al.
BCR-DRL: Behavior- and Context-aware Reward for Deep Reinforcement Learning in Human-AI Coordination
by Xin Hao, Bahareh Nakisa, Mohmmad Naim Rastgoo, Richard Dazeley, Gaoyang Pang
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed behavior- and context-aware reward (BCR) for deep reinforcement learning (DRL) addresses two critical challenges in human-AI coordination: sparse rewards and unpredictable human behaviors. The BCR optimizes exploration and exploitation by leveraging human behaviors and contextual information, comprising a novel dual intrinsic rewarding scheme and a new context-aware weighting mechanism. This approach enhances the capture of sparse rewards and improves exploitation, as demonstrated through extensive simulations in the Overcooked environment, achieving approximately 20% increase in cumulative sparse rewards and a 67% reduction in convergence time compared to state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way for AI agents to work with humans. It solves two big problems: getting rewards when they’re rare and dealing with unpredictable human behavior. The solution is called the “behavior- and context-aware reward” (BCR). It has two parts: one helps the AI agent explore more effectively, and the other helps it make good decisions based on what’s happening in the moment. This approach makes a big difference, showing that it can get 20% more rewards and work 67% faster than current methods. |
Keywords
* Artificial intelligence * Reinforcement learning
