Search Results for author: Tom Eccles

Found 11 papers, 6 papers with code

Human-Agent Cooperation in Bridge Bidding

no code implementations28 Nov 2020 Edward Lockhart, Neil Burch, Nolan Bard, Sebastian Borgeaud, Tom Eccles, Lucas Smaira, Ray Smith

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.

Imitation Learning reinforcement-learning +1

Biases for Emergent Communication in Multi-agent Reinforcement Learning

no code implementations NeurIPS 2019 Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel

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

no code implementations25 Sep 2019 Thomas Anthony, Ian Gemp, Janos Kramar, Tom Eccles, Andrea Tacchetti, Yoram Bachrach

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

no code implementations19 Mar 2019 Tom Eccles, Edward Hughes, János Kramár, Steven Wheelwright, Joel Z. Leibo

We analyse the resulting policies to show that the reciprocating agents are strongly influenced by their co-players' behavior.

Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies

1 code implementation NeurIPS 2018 Alessandro Achille, Tom Eccles, Loic Matthey, Christopher P. Burgess, Nick Watters, Alexander Lerchner, Irina Higgins

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.

Representation Learning

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