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Found 6 papers, 0 papers with code
Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data
We revisit the problem of offline reinforcement learning with value function realizability but without Bellman completeness.
Linear Reinforcement Learning with Ball Structure Action Space
We study the problem of Reinforcement Learning (RL) with linear function approximation, i. e. assuming the optimal action-value function is linear in a known $d$-dimensional feature mapping.
Intrinsic Dimension Estimation Using Wasserstein Distances
It has long been thought that high-dimensional data encountered in many practical machine learning tasks have low-dimensional structure, i. e., the manifold hypothesis holds.
Model-Based Reinforcement Learning with Value-Targeted Regression
We propose a model based RL algorithm that is based on optimism principle: In each episode, the set of models that are `consistent' with the data collected is constructed.
PROVABLY BENEFITS OF DEEP HIERARCHICAL RL
Modern complex sequential decision-making problem often both low-level policy and high-level planning.
Feature-Based Q-Learning for Two-Player Stochastic Games
Consider a two-player zero-sum stochastic game where the transition function can be embedded in a given feature space.
