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Found 6 papers, 0 papers with code
Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs
Our result is the first that is minimax optimal (up to log factors) in all parameters $S, A, H$ and $\epsilon$, improving on existing work that either assumes uniformly bounded mixing times for all policies or has suboptimal dependence on the parameters.
Span-Based Optimal Sample Complexity for Average Reward MDPs
Our result is the first that is minimax optimal (up to log factors) in all parameters $S, A, H$ and $\varepsilon$, improving on existing work that either assumes uniformly bounded mixing times for all policies or has suboptimal dependence on the parameters.
Clustering Without an Eigengap
Our gap-free clustering procedure also leads to improved algorithms for recursive clustering.
Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces
This latter objective is called stability and is especially important when the state space is unbounded, such that the states can be arbitrarily far from each other and the agent can drift far away from the desired states.
The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types
In this work, we advocate that grounding the rationality coefficient in real data for each feedback type, rather than assuming a default value, has a significant positive effect on reward learning.
Situational Confidence Assistance for Lifelong Shared Autonomy
Shared autonomy enables robots to infer user intent and assist in accomplishing it.
