Summary of Diversity Progress For Goal Selection in Discriminability-motivated Rl, by Erik M. Lintunen et al.
Diversity Progress for Goal Selection in Discriminability-Motivated RL
by Erik M. Lintunen, Nadia M. Ady, Christian Guckelsberger
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 The proposed “Diversity Progress” (DP) method improves reinforcement learning by introducing non-uniform goal selection, outperforming uniform-random selection in intrinsically-motivated goal-conditioned RL. DP learners form a curriculum based on observed improvement in discriminability over their set of goals, motivating the agent to learn diverse skills without extrinsic rewards. This approach is applicable to discriminability-motivated agents, where intrinsic reward is computed as a function of certainty about pursuing true goals. Empirical results show that DP-motivated agents learn distinguishable skills faster than previous approaches and avoid goal distribution collapse. The proof-of-concept demonstrates the potential for learning complex skills without extrinsic rewards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to improve reinforcement learning by choosing goals in a more intelligent way. Instead of randomly choosing goals, the method learns how to choose goals that help it get better at doing things. This helps the agent learn many different skills without needing extra rewards. The results show that this approach is better than others and can avoid some common problems. |
Keywords
* Artificial intelligence * Reinforcement learning
