In the competitive arena of Multi-Agent Reinforcement Learning (MARL), progress has long been bottlenecked by human intuition. For years, researchers have manually refined algorithms like Counterfactual Regret Minimization (CFR) and Policy Space Response Oracles (PSRO), navigating a vast combinatorial space of update rules via trial-and-error. Google DeepMind research team has now shifted this paradigm with
The post Google DeepMind Researchers Apply Semantic Evolution to Create Non Intuitive VAD-CFR and SHOR-PSRO Variants for Superior Algorithmic Convergence appeared first on MarkTechPost. Read More
