Summary of Offline Model-based Reinforcement Learning with Anti-exploration, by Padmanaba Srinivasan et al.
Offline Model-Based Reinforcement Learning with Anti-Exploration
by Padmanaba Srinivasan, William Knottenbelt
First submitted to arxiv on: 20 Aug 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 In this paper, researchers present a novel approach to offline reinforcement learning (RL) called Morse Model-based offline RL (MoMo). MoMo combines the benefits of both model-free and model-based methods by utilizing an anti-exploration paradigm to prevent value overestimation. The authors also introduce a policy constraint and truncation function to handle out-of-distribution (OOD) states. Experimental results show that both the model-free and model-based variants of MoMo outperform existing baselines on various D4RL datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline reinforcement learning is a challenging problem where data may be limited in quantity and quality. To address this issue, researchers have developed model-based reinforcement learning (MBRL) algorithms that learn a dynamics model from collected data to generate synthetic trajectories for faster learning. However, practical approaches often rely on ensembles of dynamics models, which can lead to uncertainty estimates varying greatly in scale, making it difficult to generalize hyperparameters across tasks. |
Keywords
» Artificial intelligence » Reinforcement learning
