Summary of Deer: a Delay-resilient Framework For Reinforcement Learning with Variable Delays, by Bo Xia et al.
DEER: A Delay-Resilient Framework for Reinforcement Learning with Variable Delays
by Bo Xia, Yilun Kong, Yongzhe Chang, Bo Yuan, Zhiheng Li, Xueqian Wang, Bin Liang
First submitted to arxiv on: 5 Jun 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 This paper proposes a framework called DEER (Delay-resilient Encoder-Enhanced RL) to address the challenge of delays in reinforcement learning tasks. Classic RL methods often assume the Markov property, but real-world scenarios can involve delays between observations and actions, causing issues with state augmentation-based solutions. The proposed DEER framework uses a pretrained encoder to map delayed states and their corresponding action sequences into hidden states, trained on delay-free environment datasets. This allows standard RL algorithms to be adapted for delayed scenarios without requiring additional modifications. The authors evaluate DEER through extensive experiments on Gym and Mujoco environments, demonstrating its superiority over state-of-the-art RL algorithms in both constant and random delay settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DEER is a new way to make reinforcement learning work better with delays. Right now, many AI systems have trouble when there’s a delay between what they see and what they can do. This makes it hard for them to learn from their mistakes. DEER fixes this by using a special kind of “memory” that helps the AI system remember what happened before the delay. This lets the AI system work better even with delays, which is important because delays are common in many real-world situations. |
Keywords
» Artificial intelligence » Encoder » Reinforcement learning
