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Found 31 papers, 23 papers with code
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
As large language models (LLMs) become increasingly prevalent across many real-world applications, understanding and enhancing their robustness to user inputs is of paramount importance.
Refining Minimax Regret for Unsupervised Environment Design
In this work, we introduce Bayesian level-perfect MMR (BLP), a refinement of the minimax regret objective that overcomes this limitation.
Multi-Agent Diagnostics for Robustness via Illuminated Diversity
In the rapidly advancing field of multi-agent systems, ensuring robustness in unfamiliar and adversarial settings is crucial.
Learning to Act without Actions
LAPO takes a first step towards pre-training powerful, generalist policies and world models on the vast amounts of videos readily available on the web.
The Generalization Gap in Offline Reinforcement Learning
Our experiments reveal that existing offline learning algorithms struggle to match the performance of online RL on both train and test environments.
Learning Curricula in Open-Ended Worlds
Deep reinforcement learning (RL) provides powerful methods for training optimal sequential decision-making agents.
minimax: Efficient Baselines for Autocurricula in JAX
This compute requirement is a major obstacle to rapid innovation for the field.
JaxMARL: Multi-Agent RL Environments in JAX
This not only enables GPU acceleration, but also provides a more flexible MARL environment, unlocking the potential for self-play, meta-learning, and other future applications in MARL.
Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design
Recently, it has been shown that it is possible to meta-learn update rules, with the hope of discovering algorithms that can perform well on a wide range of RL tasks.
Stabilizing Unsupervised Environment Design with a Learned Adversary
As a result, we make it possible for PAIRED to match or exceed state-of-the-art methods, producing robust agents in several established challenging procedurally-generated environments, including a partially-observed maze navigation task and a continuous-control car racing environment.
Anomaly Detection with Score Distribution Discrimination
In this paper, we propose to optimize the anomaly scoring function from the view of score distribution, thus better retaining the diversity and more fine-grained information of input data, especially when the unlabeled data contains anomaly noises in more practical AD scenarios.
Reward-Free Curricula for Training Robust World Models
We consider robustness in terms of minimax regret over all environment instantiations and show that the minimax regret can be connected to minimising the maximum error in the world model across environment instances.
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs
This results in an algorithm which sets a new state of the art across 16 tasks from the MiniHack suite used in prior work, and also performs robustly on Habitat and Montezuma's Revenge.
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning
Open-ended learning methods that automatically generate a curriculum of increasingly challenging tasks serve as a promising avenue toward generally capable reinforcement learning agents.
Weakly Supervised Anomaly Detection: A Survey
Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
We call the collected dataset the Human ChatGPT Comparison Corpus (HC3).
General Intelligence Requires Rethinking Exploration
We are at the cusp of a transition from "learning from data" to "learning what data to learn from" as a central focus of artificial intelligence (AI) research.
Exploration via Elliptical Episodic Bonuses
In recent years, a number of reinforcement learning (RL) methods have been proposed to explore complex environments which differ across episodes.
GriddlyJS: A Web IDE for Reinforcement Learning
Progress in reinforcement learning (RL) research is often driven by the design of new, challenging environments -- a costly undertaking requiring skills orthogonal to that of a typical machine learning researcher.
Grounding Aleatoric Uncertainty for Unsupervised Environment Design
Problematically, in partially-observable or stochastic settings, optimal policies may depend on the ground-truth distribution over aleatoric parameters of the environment in the intended deployment setting, while curriculum learning necessarily shifts the training distribution.
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.
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.
