Coding Multi-Agent Reinforcement Learning with PyTorch and JAX - Including ReDel and AgentScope Frameworks

Explore a comprehensive video tutorial showcasing multiple code implementations for multi-agent Reinforcement Learning (RL) systems, drawing from prestigious institutions like Stanford, UC Berkeley, OpenAI, Cohere, and Azure. Master both PyTorch and JAX implementations for developing multi-modal multi-agent RL systems, progressing from sequential core concepts to distributed parallel architectures. Discover two cutting-edge open-source frameworks released in August 2024: ReDel, a toolkit for LLM-powered recursive multi-agent systems, and AgentScope, designed for very large-scale multi-agent simulations. Gain hands-on experience through 20 different code examples that demonstrate practical applications and implementation strategies for multi-agent RL systems.