Local Updates, Global Learning (LUGL): Playing Games with non-incremental Learners
cs.LG, cs.AI
Submitted: 2026-09-03
Updated: 2026-09-03
Comments: 12 pages, 6 figures
Code: https://github.com/milecdav/Deep-CFR-2025
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Proximal Policy Optimization Algorithms
- Prioritized Experience Replay
- Othello is Solved
- Single Deep Counterfactual Regret Minimization
- DREAM: Deep Regret minimization with Advantage baselines and Model-free learning
- OpenSpiel: A Framework for Reinforcement Learning in Games
- Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
- Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks