Summary of A Temporally Correlated Latent Exploration For Reinforcement Learning, by Sumin Oh et al.
A Temporally Correlated Latent Exploration for Reinforcement Learning
by SuMin Oh, WanSoo Kim, HyunJin Kim
First submitted to arxiv on: 6 Dec 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 The paper proposes a novel intrinsic reward formulation called Temporally Correlated Latent Exploration (TeCLE) to tackle the problem of efficient exploration in deep reinforcement learning. TeCLE employs an action-conditioned latent space and temporal correlation to estimate the probability distribution of states, avoiding excessive intrinsic rewards for unpredictable states. The approach injects temporal correlation into intrinsic reward computation, which determines the exploratory behaviors of agents. Experiments show that the environment where the agent performs well depends on the amount of temporal correlation. TeCLE is robust to Noisy TV and stochasticity in benchmark environments, including Minigrid and Stochastic Atari. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in machine learning called “efficient exploration”. Right now, machines don’t explore their world very well, so they can’t learn as much as they could. To fix this, the paper introduces a new way to make machines more curious. This new method is called TeCLE and it uses special math to help machines figure out what’s important to learn about. It makes machines be more clever in how they explore their world. The results show that this new method works really well in different environments. |
Keywords
* Artificial intelligence * Latent space * Machine learning * Probability * Reinforcement learning
