Summary of Memory Sequence Length Of Data Sampling Impacts the Adaptation Of Meta-reinforcement Learning Agents, by Menglong Zhang et al.
Memory Sequence Length of Data Sampling Impacts the Adaptation of Meta-Reinforcement Learning Agents
by Menglong Zhang, Fuyuan Qian, Quanying Liu
First submitted to arxiv on: 18 Jun 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 A meta-reinforcement learning (meta-RL) framework is proposed to enable fast adaptation to new tasks for embodied agents in real-world environments. The study focuses on off-policy meta-RL algorithms that rely heavily on efficient data sampling strategies to extract and represent historical trajectories. Two types of off-policy meta-RL algorithms, based on Thompson sampling and Bayes-optimality theories, are investigated in continuous control tasks within the MuJoCo environment and sparse reward navigation tasks. The results show that different data sampling methods impact the exploration and adaptability of meta-RL agents, with the algorithm based on Bayes-optimality theory exhibiting more robust and better adaptability than the algorithm based on Thompson sampling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Meta-reinforcement learning helps robots learn new skills quickly. The research looks at how different ways of collecting data affect a robot’s ability to learn and adapt in new situations. Two types of algorithms are tested: one based on “Thompson sampling” and another based on “Bayes-optimality”. The results show that the way data is collected matters, and using the right method can make a big difference. |
Keywords
» Artificial intelligence » Reinforcement learning
