Understanding the challenges of convergence and performance in multi-agent learning is crucial.
Unsupervised environment design (UD) can lead to agents generalizing corner cases.
Combining language models with multi-agent learning can explore the emergence of novel skills and capabilities.
Deep dives
Finding Blind Spots in Research Landscape
Jacob Forrester's research focus is identifying the blind spots in the research landscape and filling them. He gave examples from his past work, such as studying multi-agent learning when it was a gap in the research. Going forward, he aims to understand the limitations of current methods and address them.
Unsupervised Environment Design in Multi-Agent Research
Jacob Forrester is fascinated by unsupervised environment design (UD) and its application in multi-agent learning. He discusses how UD involves discovering environment distributions that lead to specific translation results and allow agents to generalize corner cases. The challenge lies in addressing the interaction of learning systems and bridging the sim-to-real gap effectively.
Challenges in Multi-Agent Reinforcement Learning
Multi-agent RL presents challenges that differ from single-agent RL. Jacob Forrester emphasizes that in multi-agent scenarios, the guarantees of convergence and performance that apply to single agents break down. Non-stationarity and equilibrium selection become key challenges, leading to unexpected phenomena like the iterated prisoner's dilemma. Understanding these challenges is crucial for effective multi-agent learning.
The Role of Multi-Agent Learning in AI Development
Jacob Forrester discusses the role of multi-agent learning in the path towards powerful AI and potentially AGI. He acknowledges that the interaction of intelligent agents has likely driven the development of intelligence throughout evolution. While current large-scale language models play a significant role in achieving human-like abilities, Forrester highlights the importance of multi-agent interaction and meta-evolution as potential avenues for surpassing human abilities.
Learning Communication Protocols in Multi-Agent RL
Jacob Forrester's classic work on learning to communicate with deep multi-agent RL explored how agents could develop communication protocols. While now models like GPT have shown promising results in language-based tasks, Forrester is interested in combining these models with multi-agent learning to explore the emergence of novel skills and capabilities.
Jakob Foerster on Multi-Agent learning, Cooperation vs Competition, Emergent Communication, Zero-shot coordination, Opponent Shaping, agents for Hanabi and Prisoner's Dilemma, and more.
Jakob Foerster is an Associate Professor at University of Oxford.
