Summary of Fast Value Tracking For Deep Reinforcement Learning, by Frank Shih et al.
Fast Value Tracking for Deep Reinforcement Learning
by Frank Shih, Faming Liang
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 sampling algorithm, Langevinized Kalman Temporal-Difference (LKTD), is introduced for deep reinforcement learning, combining the Kalman filtering paradigm with Stochastic Gradient Markov Chain Monte Carlo (SGMCMC). LKTD efficiently generates posterior samples from the distribution of deep neural network parameters, enabling uncertainty quantification and monitoring during policy updates. This leads to more robust and adaptable reinforcement learning approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning helps machines make good decisions by interacting with their environment. Current methods often think about problems as one-time events, not considering how uncertain things are or how the machine will learn over time. Our new algorithm, LKTD, uses ideas from statistics to create a better way of learning and making decisions. It lets us see how sure we are of our predictions and how they might change as we learn more. |
Keywords
* Artificial intelligence * Neural network * Reinforcement learning
