Summary of Variable-agnostic Causal Exploration For Reinforcement Learning, by Minh Hoang Nguyen et al.
Variable-Agnostic Causal Exploration for Reinforcement Learning
by Minh Hoang Nguyen, Hung Le, Svetha Venkatesh
First submitted to arxiv on: 17 Jul 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 introduces Variable-Agnostic Causal Exploration for Reinforcement Learning (VACERL), a novel framework that leverages causal relationships to drive exploration in reinforcement learning without specifying environmental causal variables. The VACERL approach identifies crucial observation-action steps associated with key variables using attention mechanisms, constructs a causal graph connecting these steps, and guides the agent towards observation-action pairs with greater causal influence on task completion. This can be used to generate intrinsic rewards or establish a hierarchy of subgoals to enhance exploration efficiency. Experimental results show significant improvements in agent performance in grid-world, 2D games, and robotic domains, particularly in scenarios with sparse rewards and noisy actions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computer programs learn from trial-and-error better. Right now, these programs (called agents) spend too much time trying things to see what works. The researchers created a new way for the agent to figure out what matters most and focus on that. They used special tools to understand how actions affect the environment and then let the agent follow this understanding. This made the agent learn faster and more efficiently in different scenarios, like games or robots. |
Keywords
* Artificial intelligence * Attention * Reinforcement learning
