Summary of Dual Action Policy For Robust Sim-to-real Reinforcement Learning, by Ng Wen Zheng Terence et al.
Dual Action Policy for Robust Sim-to-Real Reinforcement Learning
by Ng Wen Zheng Terence, Chen Jianda
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 The proposed Dual Action Policy (DAP) addresses the dynamics mismatch in reinforcement learning by decoupling task rewards in simulation from domain adaptation via reward adjustments. DAP uses a single policy to predict two sets of actions: one for maximizing task rewards and another for adapting to the target environment. This approach enables more effective training in the source domain and enhances agent robustness through uncertainty-based exploration. Experimental results show that DAP outperforms baselines on challenging tasks in simulation, demonstrating its effectiveness in bridging the sim-to-real gap. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DAP is a new way to make reinforcement learning work better when moving from simulated environments to real-world situations. It does this by treating two types of actions separately: one for getting good rewards in simulations and another for adjusting to the new environment. This makes training more effective and helps agents be more robust. The results show that DAP works well on hard tasks in simulation, which is important for making progress in sim-to-real transfer. |
Keywords
» Artificial intelligence » Domain adaptation » Reinforcement learning
