Summary of Entropy Regularized Task Representation Learning For Offline Meta-reinforcement Learning, by Mohammadreza Nakhaei et al.
Entropy Regularized Task Representation Learning for Offline Meta-Reinforcement Learning
by Mohammadreza Nakhaei, Aidan Scannell, Joni Pajarinen
First submitted to arxiv on: 19 Dec 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 The proposed offline meta-reinforcement learning framework enables agents to rapidly adapt to new tasks by training on data from a set of different tasks. Context-based approaches utilize a history of state-action-reward transitions, referred to as the context, to infer representations of the current task and condition the agent on the task representations. However, these approaches suffer from distribution mismatch, causing overfitting to offline training data. To address this issue, the authors approximately minimize the mutual information between the task representations and behavior policy by maximizing the entropy of the behavior policy conditioned on the task representations. The approach is validated in MuJoCo environments, demonstrating improved performance in both in-distribution and out-of-distribution tasks compared to baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline meta-reinforcement learning helps robots quickly learn new tasks by training them on data from different tasks. Current methods use a “memory” of what happened before to understand the current task. However, this memory doesn’t always match what happens at test time, so they overfit to the training data. The authors fixed this problem by making the behavior policy (how the agent behaves) more random when given information about the task. This approach works better than previous methods in simulations. |
Keywords
» Artificial intelligence » Overfitting » Reinforcement learning
