Jakob Foerster

Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

2019 · International Conference on Machine Learning

When observing the actions of others, humans make inferences about why they acted as they did, and what this implies about the world; humans also use the fact that their actions will be interpreted in …

Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning

2019 · arXiv (Cornell University)

In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota. One common aspect of all …

Replay-Guided Adversarial Environment Design

2021 · arXiv (Cornell University)

Deep reinforcement learning (RL) agents may successfully generalize to new settings if trained on an appropriately diverse set of environment and task configurations. Unsupervised Environment Design (UED) is a promising self-supervised RL paradigm, wherein the …

JaxLife: An Open-Ended Agentic Simulator

2024 · arXiv (Cornell University)

Human intelligence emerged through the process of natural selection and evolution on Earth. We investigate what it would take to re-create this process in silico. While past work has often focused on low-level processes (such …