Summary of Inverse Reinforcement Learning From Non-stationary Learning Agents, by Kavinayan P. Sivakumar et al.
Inverse Reinforcement Learning from Non-Stationary Learning Agents
by Kavinayan P. Sivakumar, Yi Shen, Zachary Bell, Scott Nivison, Boyuan Chen, Michael M. Zavlanos
First submitted to arxiv on: 18 Oct 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 an inverse reinforcement learning (IRL) method to learn the reward function of a learning agent using trajectory data. The approach, called bundle behavior cloning, uses a small number of trajectories generated by the agent’s policy at different points in time to learn a set of policies that match the distribution of actions observed. These cloned policies are then used to train a neural network model that estimates the reward function. The proposed method outperforms standard behavior cloning and is validated through numerical experiments on a reinforcement learning problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special kind of machine learning called inverse reinforcement learning to figure out what rewards an agent wants when it’s trying to learn something new. They developed a new way to do this using “bundle behavior cloning”, which looks at how the agent acts over time and tries to match that with some reward function. This helps us understand why the agent is doing what it’s doing, and could be useful for all sorts of situations where we want to understand someone else’s goals. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Reinforcement learning
