Proto Successor Measure: Representing the Space of All Possible Solutions of Reinforcement Learning

2024-11-29

arXiv.org

AI Summary

This work addresses zero-shot transfer in reinforcement learning: enabling an agent to instantaneously generate an optimal policy for any downstream reward function using only a single environment interaction—without further sampling. To this end, the authors introduce the *Prototype Successor Measure* (PSM), the first policy-agnostic, complete basis set that fully characterizes the solution space of all optimal policies; they prove theoretically that any optimal policy can be exactly represented as an affine combination of PSM basis elements. Methodologically, the approach unifies successor features with measure theory, learning the PSM self-supervisedly in a model-free manner and solving for linear weights online. Experiments demonstrate that the framework achieves truly task-agnostic zero-shot policy generation on standard MDPs, overcoming prior zero-shot RL methods’ reliance on task structure or MDP-specific priors.

Technology Category

• Machine Learning: Reinforcement Learning
• Multiagent Systems: Multiagent Learning
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Having explored an environment, intelligent agents should be able to transfer their knowledge to most downstream tasks within that environment. Referred to as"zero-shot learning,"this ability remains elusive for general-purpose reinforcement learning algorithms. While recent works have attempted to produce zero-shot RL agents, they make assumptions about the nature of the tasks or the structure of the MDP. We present emph{Proto Successor Measure}: the basis set for all possible solutions of Reinforcement Learning in a dynamical system. We provably show that any possible policy can be represented using an affine combination of these policy independent basis functions. Given a reward function at test time, we simply need to find the right set of linear weights to combine these basis corresponding to the optimal policy. We derive a practical algorithm to learn these basis functions using only interaction data from the environment and show that our approach can produce the optimal policy at test time for any given reward function without additional environmental interactions. Project page: https://agarwalsiddhant10.github.io/projects/psm.html.

Problem

• Enabling zero-shot learning in reinforcement learning agents.
• Representing all possible behaviors of RL agents in dynamical systems.
• Learning basis functions for optimal policy without additional interactions.

Innovation

• Proto Successor Measure represents RL agent behaviors
• Affine combination of basis functions for behaviors
• Reward-free learning optimizes policies without interactions