Reinforcement Learning
Diffusion-Reward Adversarial Imitation Learning
Despite its encouraging results, GAIL training is often brittle and unstable.
Effective Exploration Based on the Structural Information Principles
The SI2E analyzes value differences in the agent's policy between state-action
Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
Based on our analysis, we propose a spectral normalization method to mitigate the exploding variance issue caused by long model unrolls.
Excluding the Irrelevant: Focusing Reinforcement Learning through Continuous Action Masking
Focusing learning on these relevant actions can significantly improve training efficiency and effectiveness.
Train Hard, Fight Easy: Robust Meta Reinforcement Learning
A significant challenge in extending this success to mass production is the variation between instances of the problem, e.g.,
Train Hard, Fight Easy: Robust Meta Reinforcement Learning
A significant challenge in extending this success to mass production is the variation between instances of the problem, e.g.,
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
As one of the most mainstream paradigms for sequential decision-making, RL has extensive applications in many real-world problems (Kober et al., 2013; Mnih et al.,
