Search Results for author:
Found 2 papers, 0 papers with code
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
In this paper, we introduce a general framework on Risk-Sensitive Distributional Reinforcement Learning (RS-DisRL), with static Lipschitz Risk Measures (LRM) and general function approximation.
Distributional Reinforcement Learning
reinforcement-learning
+1
Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization
Learning Markov decision processes (MDP) in an adversarial environment has been a challenging problem.
