Policy Learning Sample Complexity Under Mu-Resets Protocol Resolved
Key takeaways
- The sample complexity of policy learning with mu-resets depends on policy realizability.
- Horizon dependence varies significantly with the coverage assumption of the reset distribution.
- Bounded all-policy concentrability leads to exponential sample complexity.
- Bounded pushforward concentrability yields a tighter exponential-square-root dependence.
Who benefits
Summary
This paper resolves the question of policy realizability's role in the sample complexity of policy-based reinforcement learning under the mu-resets interaction protocol. It establishes tight bounds for horizon dependence, showing exponential complexity under bounded all-policy concentrability and a tighter exponential-square-root dependence under bounded pushforward concentrability.
Why it matters
For AI researchers and algorithm designers, understanding these fundamental sample complexity bounds is crucial for developing more efficient and scalable reinforcement learning algorithms, especially in scenarios where data acquisition is costly.
How to implement this in your domain
- 1Consider the implications of different concentrability assumptions when designing or analyzing RL algorithms for specific applications.
- 2Evaluate the trade-offs between policy realizability and sample efficiency in your RL problem formulations.
- 3Use these theoretical bounds to guide the design of data collection strategies for policy learning.
- 4Explore methods to improve the coverage of reset distributions to potentially reduce sample complexity.
Original post by Gene Li
"arXiv:2608.07772v1 Announce Type: new Abstract: We study policy-based reinforcement learning under the $\mu$-resets interaction protocol of Kakade and Langford [KL02]. This interaction protocol enables the learner to sample trajectories from a given exploratory reset distribution…"
View on XOriginally posted by Gene Li on X · view source
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