Summary of Logical Specifications-guided Dynamic Task Sampling For Reinforcement Learning Agents, by Yash Shukla et al.
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents
by Yash Shukla, Tanushree Burman, Abhishek Kulkarni, Robert Wright, Alvaro Velasquez, Jivko Sinapov
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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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 Logical Specifications-guided Dynamic Task Sampling (LSTS) approach learns a set of reinforcement learning (RL) policies to guide an agent from an initial state to a goal state based on high-level task specifications. Unlike previous work, LSTS does not assume information about the environment dynamics or Reward Machines, and dynamically samples promising tasks that lead to successful goal policies. The method is evaluated on gridworlds, partially observable robotic tasks, and continuous control robotic manipulation tasks, achieving improved time-to-threshold performance compared to state-of-the-art baselines such as Q-Learning for Reward Machines (RM) and Compositional RL from logical Specifications (DIRL). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps artificial agents learn new behaviors using a special kind of guidance. Think of it like a roadmap that tells the agent how to get from one place to another. The old way of doing this required a lot of interactions with the environment, but this new approach is more efficient and gets better results. It works by breaking down big tasks into smaller ones and picking the most promising path to follow. |
Keywords
* Artificial intelligence * Reinforcement learning
