Summary of Generalized Bayesian Deep Reinforcement Learning, by Shreya Sinha Roy et al.
Generalized Bayesian deep reinforcement learning
by Shreya Sinha Roy, Richard G. Everitt, Christian P. Robert, Ritabrata Dutta
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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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 In this paper, researchers propose a novel approach to Bayesian reinforcement learning (BRL) that combines principles from Bayesian statistics and reinforcement learning. The method involves inferring the posterior distribution of the data generating process (DGP) modeling the true environment and policy learning using the learned posterior. To model the dynamics of the unknown environment, they use deep generative models assuming Markov dependence. The authors also introduce a novel scoring rule posterior to train these models without likelihood functions. They employ sequential Monte Carlo (SMC) samplers to draw samples from this generalized Bayesian posterior distribution and utilize gradient-based Markov chain Monte Carlo (MCMC) kernels within SMC for scalability in high-dimensional neural networks. Furthermore, the authors prove a Bernstein-von Misses type theorem justifying the use of the prequential scoring rule posterior. For policy learning, they propose expected Thompson sampling (ETS) to learn the optimal policy by maximizing the expected value function with respect to the posterior distribution. This approach improves upon traditional Thompson sampling (TS) and its extensions. The authors demonstrate the effectiveness of their method through simulation studies and extend it to a challenging problem with continuous action space without theoretical guarantees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists are working on a new way to make decisions in uncertain situations using something called Bayesian reinforcement learning. They’re trying to figure out what’s going on in an environment that might be changing or unpredictable. To do this, they use special computer models that can learn from experience and make predictions about the future. The authors also developed a new way to train these models without having all the information upfront. They tested their approach using simulated scenarios and showed that it works better than some other methods. This could have important implications for things like self-driving cars or robots that need to adapt to changing situations. |
Keywords
» Artificial intelligence » Likelihood » Reinforcement learning
