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That Escalated Quickly: Compounding Complexity by Editing Levels at the Frontier of Agent Capabilities
Jack Parker-Holder · Minqi Jiang · Michael Dennis · Mikayel Samvelyan · Jakob Foerster · Edward Grefenstette · Tim Rocktäschel

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in Deep Reinforcement Learning »
Event URL: https://openreview.net/forum?id=3qGInPFqR0p »

Deep Reinforcement Learning (RL) has recently produced impressive results in a series of settings such as games and robotics. However, a key challenge that limits the utility of RL agents for real-world problems is the agent's ability to generalize to unseen variations (or levels). To train more robust agents, the field of Unsupervised Environment Design (UED) seeks to produce a curriculum by updating both the agent and the distribution over training environments. Recent advances in UED have come from promoting levels with high regret, which provides theoretical guarantees in equilibrium and empirically has been shown to produce agents capable of zero-shot transfer to unseen human-designed environments. However, current methods require either learning an environment-generating adversary, which remains a challenging optimization problem, or curating a curriculum from randomly sampled levels, which is ineffective if the search space is too large. In this paper we instead propose to evolve a curriculum, by making edits to previously selected levels. Our approach, which we call Adversarially Compounding Complexity by Editing Levels (ACCEL), produces levels at the frontier of an agent's capabilities, resulting in curricula that start simple but become increasingly complex. ACCEL maintains the theoretical benefits of prior works, while outperforming them empirically when transferring to complex out-of-distribution environments.

Author Information

Jack Parker-Holder (University of Oxford)
Minqi Jiang (UCL & FAIR)
Michael Dennis (University of California Berkeley)

Michael Dennis is a 5th year grad student at the Center for Human-Compatible AI. With a background in theoretical computer science, he is working to close the gap between decision theoretic and game theoretic recommendations and the current state of the art approaches to robust RL and multi-agent RL. The overall aim of this work is to ensure that our systems behave in a way that is robustly beneficial. In the single agent setting, this means making decisions and managing risk in the way the designer intends. In the multi-agent setting, this means ensuring that the concerns of the designer and those of others in the society are fairly and justly negotiated to the benefit of all involved.

Mikayel Samvelyan (University College London)
Jakob Foerster (University of Oxford)

Jakob Foerster received a CIFAR AI chair in 2019 and is starting as an Assistant Professor at the University of Toronto and the Vector Institute in the academic year 20/21. During his PhD at the University of Oxford, he helped bring deep multi-agent reinforcement learning to the forefront of AI research and interned at Google Brain, OpenAI, and DeepMind. He has since been working as a research scientist at Facebook AI Research in California, where he will continue advancing the field up to his move to Toronto. He was the lead organizer of the first Emergent Communication (EmeCom) workshop at NeurIPS in 2017, which he has helped organize ever since.

Edward Grefenstette (Facebook AI Research & University College London)
Tim Rocktäschel (Facebook AI Research)

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