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Found 12 papers, 6 papers with code
Insights From the NeurIPS 2021 NetHack Challenge
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge.
Evolving Curricula with Regret-Based Environment Design
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.
Improving Intrinsic Exploration with Language Abstractions
Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse.
Replay-Guided Adversarial Environment Design
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.
That Escalated Quickly: Compounding Complexity by Editing Levels at the Frontier of Agent Capabilities
Deep Reinforcement Learning (RL) has recently produced impressive results in a series of settings such as games and robotics.
Grounding Aleatoric Uncertainty in Unsupervised Environment Design
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
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.
Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay
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.
Resolving Causal Confusion in Reinforcement Learning via Robust Exploration
To address this, we present a robust exploration strategy which enables causal hypothesis-testing by interaction with the environment.
Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning
In this work, we show that we can incorporate relational inductive biases, encoded in the form of relational graphs, into agents.
Prioritized Level Replay
Environments with procedurally generated content serve as important benchmarks for testing systematic generalization in deep reinforcement learning.
WordCraft: An Environment for Benchmarking Commonsense Agents
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.
