Summary of Transfer Reinforcement Learning in Heterogeneous Action Spaces Using Subgoal Mapping, by Kavinayan P. Sivakumar et al.
Transfer Reinforcement Learning in Heterogeneous Action Spaces using Subgoal Mapping
by Kavinayan P. Sivakumar, Yan Zhang, Zachary Bell, Scott Nivison, Michael M. Zavlanos
First submitted to arxiv on: 18 Oct 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 transfer reinforcement learning method that enables agents with different action spaces to learn from each other. The goal is to use an expert agent’s successful demonstration of a task to train a learner agent in its own action space, reducing the number of samples required for learning. The existing methods require either handcrafted mappings or sharing policy parameters between agents, which can introduce bias and lack generalizability. The proposed method learns a subgoal mapping between the expert and learner agents’ policies using a Long Short Term Memory (LSTM) network trained on a distribution of tasks. This mapping is used to predict the learner’s subgoal sequence for unseen tasks, improving learning speed by biasing the agent’s policy towards the predicted sequence. Numerical experiments demonstrate the effectiveness of the proposed scheme in finding the underlying subgoal mapping and improving sample efficiency and training time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps machines learn from each other even if they do different things. Imagine you’re trying to teach a robot how to do something, but it has its own special way of doing it. This makes learning harder because the robot doesn’t understand what’s important or what the goal is. The researchers found a solution by creating a map that connects the expert and learner robots’ actions together. They used this map to help the learner robot learn faster and more efficiently. With this new method, machines can learn from each other better and make fewer mistakes. |
Keywords
» Artificial intelligence » Lstm » Reinforcement learning
