Search Results for author:
Found 13 papers, 4 papers with code
A theory of appropriateness with applications to generative artificial intelligence
Humans navigate a multi-scale mosaic of interlocking notions of what is appropriate for different situations.
A Review of Cooperation in Multi-agent Learning
Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous disciplines, including game theory, economics, social sciences, and evolutionary biology.
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition
The algorithm we propose consists of two parts: an agent architecture and a learning rule.
Melting Pot 2.0
Melting Pot is a research tool developed to facilitate work on multi-agent artificial intelligence, and provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios.
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot
Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess generalization to novel situations as their primary objective (unlike supervised-learning benchmarks).
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Learning to Incentivize Other Learning Agents
The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years.
Malthusian Reinforcement Learning
Here we explore a new algorithmic framework for multi-agent reinforcement learning, called Malthusian reinforcement learning, which extends self-play to include fitness-linked population size dynamics that drive ongoing innovation.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Value-Decomposition Networks For Cooperative Multi-Agent Learning
We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal.
Ranked #1 on
SMAC+
on Off_Superhard_parallel
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Deep Reinforcement Learning in Large Discrete Action Spaces
Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems.
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
Further, we use deep deterministic policy gradients to learn a policy that for each position of the slate, guides attention towards the part of the action space in which the value is the highest and we only evaluate actions in this area.
The Sample-Complexity of General Reinforcement Learning
We present a new algorithm for general reinforcement learning where the true environment is known to belong to a finite class of N arbitrary models.
On Nicod's Condition, Rules of Induction and the Raven Paradox
For us, "(objective) background knowledge" is restricted to information that can be expressed as probability events.
Concentration and Confidence for Discrete Bayesian Sequence Predictors
We prove tight high-probability bounds on the cumulative error, which is measured in terms of the Kullback-Leibler (KL) divergence.
