Reinforcement Learning
Normalization and effective learning rates in reinforcement learning Clare Lyle
Layer normalization has demonstrated remarkable effectiveness at preventing plasticity loss in continual and reinforcement learning (RL), though the precise reasons for this effectiveness remain mysterious.
Multi-Reward Best Policy Identification
This bound guides the design of an optimal exploration policy attaining minimal sample complexity. However, this lower bound involves solving a hard non-convex optimization problem.
Suggesting Variable Order for Cylindrical Algebraic Decomposition via Reinforcement Learning
The other is GRL-SVO(NUP), a fast heuristic providing a total order directly.
Reinforcement learning from Human Feedback (RLHF) learns from preference signals, while standard Reinforcement Learning (RL) directly learns from reward
The latter case can be further reduced to adversarial MDP when preferences only depend on the final state.
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
A common feature of these applications is that there are multiple decision makers interacting with each other in a common environment.
DIFFER: Decomposing Individual Reward for Fair Experience Replay in Multi-Agent Reinforcement Learning
The code is available in https://github.com/cathyhxh/DIFFER.