Shimon Whiteson

Multi-Objective Deep Reinforcement Learning

2016 · arXiv (Cornell University)

We propose Deep Optimistic Linear Support Learning (DOL) to solve high-dimensional multi-objective decision problems where the relative importances of the objectives are not known a priori. Using features from the high-dimensional inputs, DOL computes the …

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 …

Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients

2021 · arXiv (Cornell University)

Policy gradient methods are an attractive approach to multi-agent reinforcement learning problems due to their convergence properties and robustness in partially observable scenarios. However, there is a significant performance gap between state-of-the-art policy gradient and …