Search Results for author: Minqi Jiang

Found 12 papers, 6 papers with code

Evolving Curricula with Regret-Based Environment Design

1 code implementation2 Mar 2022 Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel

Our approach, which we call Adversarially Compounding Complexity by Editing Levels (ACCEL), seeks to constantly produce levels at the frontier of an agent's capabilities, resulting in curricula that start simple but become increasingly complex.

Replay-Guided Adversarial Environment Design

no code implementations NeurIPS 2021 Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel

Furthermore, our theory suggests a highly counterintuitive improvement to PLR: by stopping the agent from updating its policy on uncurated levels (training on less data), we can improve the convergence to Nash equilibria.

Grounding Aleatoric Uncertainty in Unsupervised Environment Design

no code implementations29 Sep 2021 Minqi Jiang, Michael D Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Kuttler, Edward Grefenstette, Tim Rocktäschel, Jakob Nicolaus Foerster

In reinforcement learning (RL), adaptive curricula have proven highly effective for learning policies that generalize well under a wide variety of changes to the environment.

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

1 code implementation27 Sep 2021 Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel

By leveraging the full set of entities and environment dynamics from NetHack, one of the richest grid-based video games, MiniHack allows designing custom RL testbeds that are fast and convenient to use.

NetHack reinforcement-learning +1

Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay

no code implementations NeurIPS Workshop ICBINB 2021 Iryna Korshunova, Minqi Jiang, Jack Parker-Holder, Tim Rocktäschel, Edward Grefenstette

Prioritized Level Replay (PLR) has been shown to induce adaptive curricula that improve the sample-efficiency and generalization of reinforcement learning policies in environments featuring multiple tasks or levels.

reinforcement-learning

Resolving Causal Confusion in Reinforcement Learning via Robust Exploration

no code implementations ICLR Workshop SSL-RL 2021 Clare Lyle, Amy Zhang, Minqi Jiang, Joelle Pineau, Yarin Gal

To address this, we present a robust exploration strategy which enables causal hypothesis-testing by interaction with the environment.

reinforcement-learning

Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning

2 code implementations8 Feb 2021 Zhengyao Jiang, Pasquale Minervini, Minqi Jiang, Tim Rocktaschel

In this work, we show that we can incorporate relational inductive biases, encoded in the form of relational graphs, into agents.

reinforcement-learning

Prioritized Level Replay

2 code implementations8 Oct 2020 Minqi Jiang, Edward Grefenstette, Tim Rocktäschel

Environments with procedurally generated content serve as important benchmarks for testing systematic generalization in deep reinforcement learning.

Systematic Generalization

WordCraft: An Environment for Benchmarking Commonsense Agents

1 code implementation ICML Workshop LaReL 2020 Minqi Jiang, Jelena Luketina, Nantas Nardelli, Pasquale Minervini, Philip H. S. Torr, Shimon Whiteson, Tim Rocktäschel

This is partly due to the lack of lightweight simulation environments that sufficiently reflect the semantics of the real world and provide knowledge sources grounded with respect to observations in an RL environment.

Knowledge Graphs reinforcement-learning +1

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