Summary of Proto Successor Measure: Representing the Behavior Space Of An Rl Agent, by Siddhant Agarwal et al.
Proto Successor Measure: Representing the Behavior Space of an RL Agent
by Siddhant Agarwal, Harshit Sikchi, Peter Stone, Amy Zhang
First submitted to arxiv on: 29 Nov 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 A recent paper in the realm of reinforcement learning tackles the challenge of “zero-shot learning,” where an intelligent agent can transfer its knowledge to most downstream tasks within an environment without additional interactions. The proposed approach, Proto Successor Measure (PSM), is a basis set for all possible behaviors of a Reinforcement Learning Agent in a dynamical system. By representing any possible behavior as an affine combination of these policy-independent basis functions, PSM enables the derivation of a practical algorithm to learn these basis functions using reward-free interaction data from the environment. This allows for the production of the optimal policy at test time for any given reward function without additional environmental interactions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Zero-shot learning is an ability that most intelligent agents should have – the power to transfer their knowledge to most downstream tasks within an environment without needing more information. While some recent attempts have been made, they often make assumptions about the nature of the tasks or the structure of the MDP. But what if we could create a special set of building blocks called Proto Successor Measure (PSM) that can be used to represent all possible behaviors? And then, use these building blocks to learn how to combine them in the right way to get the optimal policy for any reward function? |
Keywords
» Artificial intelligence » Reinforcement learning » Zero shot
