Summary of Proximal Curriculum with Task Correlations For Deep Reinforcement Learning, by Georgios Tzannetos et al.
Proximal Curriculum with Task Correlations for Deep Reinforcement Learning
by Georgios Tzannetos, Parameswaran Kamalaruban, Adish Singla
First submitted to arxiv on: 3 May 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 Reinforcement learning (RL) can be accelerated by carefully designing the learning curriculum, which selects tasks for an agent to perform. Traditional approaches often require task-specific tuning or are only suitable for specific objectives. This paper proposes a novel curriculum, ProCuRL-Target, that balances task difficulty and leverages correlations between tasks to progress the agent’s learning towards a target distribution. Theoretical analysis using REINFORCE learner model supports the strategy, which is experimentally validated across various domains with challenging target tasks. Our proposed curriculum outperforms state-of-the-art baselines in accelerating training for deep RL agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine helping an artificial intelligence agent learn new skills by choosing the right problems to solve first. This paper introduces a new way to design learning curricula, making it faster and more effective for AI agents to master complex tasks. The approach balances the difficulty of each task and uses connections between tasks to help the agent get closer to its goal. The idea is tested with different AI systems and shown to be better than current methods. |
Keywords
» Artificial intelligence » Reinforcement learning
