Reinforcement Learning
Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation
As a prominent category of imitation learning methods, adversarial imitation learning (AIL) has garnered significant practical success powered by neural network approximation. However, existing theoretical studies on AIL are primarily limited to simplified scenarios such as tabular and linear function approximation and involve complex algorithmic designs that hinder practical implementation, highlighting a gap between theory and practice.
Adaptive Exploration for Data-Efficient General Value Function Evaluations
General V alue Functions (GVFs) (Sutton et al., 2011) represent predictive knowledge in reinforcement learning.
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds
Adaptivity to the difficulties of a problem is a key property in sequential decision-making problems to broaden the applicability of algorithms.
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds
Adaptivity to the difficulties of a problem is a key property in sequential decision-making problems to broaden the applicability of algorithms.
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
Easily integrated, FamO2O statistically enhances existing algorithms' performance.
Reinforcement Learning with Lookahead Information
In reinforcement learning (RL), agents sequentially interact with a changing environment, aiming to collect as much reward as possible.