Summary of Maximum Entropy Reinforcement Learning Via Energy-based Normalizing Flow, by Chen-hao Chao et al.
Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow
by Chen-Hao Chao, Chien Feng, Wei-Fang Sun, Cheng-Kuang Lee, Simon See, Chun-Yi Lee
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 novel Maximum-Entropy (MaxEnt) Reinforcement Learning (RL) framework, dubbed Energy-Based Normalizing Flows (EBFlow), is introduced in this paper. This framework integrates policy evaluation and improvement steps into a single objective training process, eliminating the need for Monte Carlo approximation. The approach enables modeling of multi-modal action distributions and efficient action sampling. Experimental results on MuJoCo benchmark suite and high-dimensional robotic tasks simulated by Omniverse Isaac Gym demonstrate superior performance compared to representative baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for machines to learn from rewards, called Maximum-Entropy Reinforcement Learning (MaxEnt RL). It’s like teaching a robot how to play a game. The current methods are based on two steps: first, the machine evaluates its actions and then improves them. This new method combines those steps into one process, making it more efficient. It also helps machines learn to do many things at once and pick the best option. To see if this works better than other approaches, the authors tested it on some robotic tasks and showed that it does perform well. |
Keywords
» Artificial intelligence » Multi modal » Reinforcement learning
