Summary of Curriculum Reinforcement Learning For Complex Reward Functions, by Kilian Freitag et al.
Curriculum Reinforcement Learning for Complex Reward Functions
by Kilian Freitag, Kristian Ceder, Rita Laezza, Knut Åkesson, Morteza Haghir Chehreghani
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper proposes a novel approach to reinforcement learning (RL) that tackles control problems by introducing a two-stage reward curriculum. This method overcomes the limitations of traditional RL methods by first maximizing a simple reward function and then transitioning to the full, complex reward. The authors provide a method based on an actor-critic framework to automatically determine the transition point between the two stages. They also introduce a flexible replay buffer that enables efficient phase transfer by reusing samples from one stage in the next. The proposed method is evaluated on the DeepMind control suite and a mobile robot scenario, achieving substantial improvements in performance compared to a baseline trained without curriculum. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to teach machines to make good decisions. Right now, teaching machines to solve problems can be hard because we need to find the right reward for them to follow. The authors came up with an idea called the “two-stage reward curriculum” that helps machines learn faster and better. They tested it on some computer simulations and a real-life robot, and it worked really well! |
Keywords
* Artificial intelligence * Reinforcement learning
