Summary of Walking the Values in Bayesian Inverse Reinforcement Learning, by Ondrej Bajgar et al.
Walking the Values in Bayesian Inverse Reinforcement Learning
by Ondrej Bajgar, Alessandro Abate, Konstantinos Gatsis, Michael A. Osborne
First submitted to arxiv on: 15 Jul 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 Bayesian inverse reinforcement learning (IRL) approach is proposed, which recovers a posterior distribution over reward functions by optimizing for an unknown reward using expert demonstrations. This resulting posterior can be used to synthesize an apprentice policy that performs well on similar tasks. The key challenge in Bayesian IRL is bridging the computational gap between the hypothesis space of possible rewards and the likelihood defined in terms of Q-values. To address this, a simple change is proposed: instead of focusing on sampling rewards, focus on working in the space of Q-values, which allows for efficient computation of gradients using Hamiltonian Monte Carlo. This leads to the development of ValueWalk, a new Markov chain Monte Carlo method that illustrates its advantages on several tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bayesian inverse reinforcement learning helps us understand how someone learned to do something really well, like playing chess or driving a car. We can use this understanding to create our own good actions by copying what the expert did. The problem is that it takes too long to figure out why the expert was doing certain things. We came up with a clever idea: instead of trying to understand why the expert chose certain rewards, we focus on understanding how those rewards led to good actions (Q-values). This makes it much faster and easier to learn from experts. |
Keywords
» Artificial intelligence » Likelihood » Reinforcement learning
