Search Results for author:
Found 11 papers, 6 papers with code
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
It has the additional complexity of requiring decision-making under imperfect information, similar to Texas hold'em poker, which has a significantly smaller game tree (on the order of $10^{164}$ nodes).
A Generalist Agent
Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs.
Ranked #1 on
Skill Generalization
on RGB-Stacking
Competition-Level Code Generation with AlphaCode
Programming is a powerful and ubiquitous problem-solving tool.
Ranked #1 on
Code Generation
on CodeContests
Human-Agent Cooperation in Bridge Bidding
We introduce a human-compatible reinforcement-learning approach to a cooperative game, making use of a third-party hand-coded human-compatible bot to generate initial training data and to perform initial evaluation.
Learning to Play No-Press Diplomacy with Best Response Policy Iteration
It also features a large combinatorial action space and simultaneous moves, which are challenging for RL algorithms.
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games
Here we argue that a systematic study of many-player zero-sum games is a crucial element of artificial intelligence research.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Biases for Emergent Communication in Multi-agent Reinforcement Learning
We study the problem of emergent communication, in which language arises because speakers and listeners must communicate information in order to solve tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Neural Design of Contests and All-Pay Auctions using Multi-Agent Simulation
In contrast to auctions designed manually by economists, our method searches the possible design space using a simulation of the multi-agent learning process, and can thus handle settings where a game-theoretic equilibrium analysis is not tractable.
Learning Reciprocity in Complex Sequential Social Dilemmas
We analyse the resulting policies to show that the reciprocating agents are strongly influenced by their co-players' behavior.
An investigation of model-free planning
The field of reinforcement learning (RL) is facing increasingly challenging domains with combinatorial complexity.
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies
Intelligent behaviour in the real-world requires the ability to acquire new knowledge from an ongoing sequence of experiences while preserving and reusing past knowledge.
