research
∙
02/02/2023
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition
Many environments contain numerous available niches of variable value, e...
share
research
∙
11/24/2022
Melting Pot 2.0
Multi-agent artificial intelligence research promises a path to develop ...
research
∙
07/14/2021
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot
Existing evaluation suites for multi-agent reinforcement learning (MARL)...
research
∙
06/10/2020
Learning to Incentivize Other Learning Agents
The challenge of developing powerful and general Reinforcement Learning ...
research
∙
12/17/2018
Malthusian Reinforcement Learning
Here we explore a new algorithmic framework for multi-agent reinforcemen...
research
∙
06/16/2017
Value-Decomposition Networks For Cooperative Multi-Agent Learning
We study the problem of cooperative multi-agent reinforcement learning w...
research
∙
12/24/2015
Deep Reinforcement Learning in Large Discrete Action Spaces
Being able to reason in an environment with a large number of discrete a...
research
∙
12/03/2015
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
Many real-world problems come with action spaces represented as feature ...
research
∙
07/12/2013
On Nicod's Condition, Rules of Induction and the Raven Paradox
Philosophers writing about the ravens paradox often note that Nicod's Co...
research
∙
06/29/2013
Concentration and Confidence for Discrete Bayesian Sequence Predictors
Bayesian sequence prediction is a simple technique for predicting future...
research
∙
09/29/2012
Optimistic Agents are Asymptotically Optimal
We use optimism to introduce generic asymptotically optimal reinforcemen...
research
∙
11/25/2011
Principles of Solomonoff Induction and AIXI
We identify principles characterizing Solomonoff Induction by demands on...
research
∙
08/18/2011
