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Dylan Cope

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

ECAI Conference 2024 Conference Paper

Channel Randomisation Methods for Zero-Shot Communication

Dylan Cope
Nandi Schoots

When agents learn to communicate via self-play the result is typically brittle communication strategies that only work with agents encountered during training. To alleviate this and train agents that can communicate with agents outside of their training communities we introduce two training-time interventions that apply to the messages sent between agents. These methods are: (1) message mutation, where messages are randomly changed; and (2) channel permutation, where random permutations are applied to the message space. These proposals are tested using a simple two-player sequential referential game in which the agents are given the opportunity to establish communicative conventions within a single episode. After training multiple sets of agents we analyse the performance of these agents when they are matched with a ‘stranger’ from another training run, i. e. their zero-shot communication performance. We find that both message mutation and channel permutation positively influence performance, and we discuss their effects.

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IJCAI Conference 2024 Conference Paper

Learning Translations: Emergent Communication Pretraining for Cooperative Language Acquisition

Dylan Cope
Peter McBurney

In Emergent Communication (EC) agents learn to communicate with one another, but the protocols that they develop are specialised to their training community. This observation led to research into Zero-Shot Coordination (ZSC) for learning communication strategies that are robust to agents not encountered during training. However, ZSC typically assumes that no prior data is available about the agents that will be encountered in the zero-shot setting. In many cases, this presents an unnecessarily hard problem and rules out communication via preestablished conventions. We propose a novel AI challenge called a Cooperative Language Acquisition Problem (CLAP) in which the ZSC assumptions are relaxed by allowing a 'joiner' agent to learn from a dataset of interactions between agents in a target community. We propose and compare two methods for solving CLAPs: Behaviour Cloning (BC), and Emergent Communication pretraining and Translation Learning (ECTL), in which an agent is trained in self-play with EC and then learns to translate between an emergent protocol and the target community's protocol.

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PRL Workshop 2023 Workshop Paper

Learning to Plan with Tree Search via Deep RL

Dylan Cope
Justin Svegliato
Stuart Russell

Tree search is an important component of many decision-making algorithms but often relies on an evaluation function that estimates the desirability of each node. In this paper, we propose to learn which nodes to expand based on a variety of object-level features. We introduce a reward function for this problem based on value of computation estimates with respect to improving the policy for the underlying problem. We apply deep reinforcement learning to this problem in an approach we call Reinforcement Learning for Tree Search (RLTS) and demonstrate that it can yield better performance than baselines in a procedurally generated environment.

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