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Found 22 papers, 5 papers with code
Concept-Based Interpretable Reinforcement Learning with Limited to No Human Labels
To overcome this limitation, we introduce a novel training scheme that enables RL algorithms to efficiently learn a concept-based policy by only querying humans to label a small set of data, or in the extreme case, without any human labels.
Interpretability in Action: Exploratory Analysis of VPT, a Minecraft Agent
Understanding the mechanisms behind decisions taken by large foundation models in sequential decision making tasks is critical to ensuring that such systems operate transparently and safely.
Unifying Interpretability and Explainability for Alzheimer's Disease Progression Prediction
Our findings show that only one of the RL methods is able to satisfactorily model disease progression, but the post-hoc explanations indicate that all methods fail to properly capture the importance of amyloid accumulation, one of the pathological hallmarks of Alzheimer's disease.
PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals
We propose an interactive training scheme, PATIENT-{\Psi}-TRAINER, for mental health trainees to practice a key skill in CBT -- formulating the cognitive model of the patient -- through role-playing a therapy session with PATIENT-{\Psi}.
BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks
Given the completion of two years of BASALT competitions, we offer to the community a formalized benchmark through the BASALT Evaluation and Demonstrations Dataset (BEDD), which serves as a resource for algorithm development and performance assessment.
MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning
Unlike existing models, MABL is capable of encoding essential global information into the latent states during training while guaranteeing the decentralized execution of learned policies.
Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition
To facilitate research in the direction of fine-tuning foundation models from human feedback, we held the MineRL BASALT Competition on Fine-Tuning from Human Feedback at NeurIPS 2022.
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
We aim to understand how people assess human likeness in navigation produced by people and artificially intelligent (AI) agents in a video game.
UniMASK: Unified Inference in Sequential Decision Problems
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks.
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning
The first algorithm, IVIPER, extends VIPER, a recent method for single-agent interpretable RL, to the multi-agent setting.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks.
Retrospective on the 2021 BASALT Competition on Learning from Human Feedback
The goal of the competition was to promote research towards agents that use learning from human feedback (LfHF) techniques to solve open-world tasks.
A Survey of Explainable Reinforcement Learning
In this survey, we propose a novel taxonomy for organizing the XRL literature that prioritizes the RL setting.
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned
With this in mind, we hosted the third edition of the MineRL ObtainDiamond competition, MineRL Diamond 2021, with a separate track in which we permitted any solution to promote the participation of newcomers.
The MineRL BASALT Competition on Learning from Human Feedback
Rather than training AI systems using a predefined reward function or using a labeled dataset with a predefined set of categories, we instead train the AI system using a learning signal derived from some form of human feedback, which can evolve over time as the understanding of the task changes, or as the capabilities of the AI system improve.
Towards robust and domain agnostic reinforcement learning competitions
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field.
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods
Because of this decision tree equivalence, any function approximator can be used during training, including a neural network, while yielding a decision tree policy for the base MDP.
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors
Although deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples, affording only a shrinking segment of the AI community access to their development.
Guaranteeing Reproducibility in Deep Learning Competitions
To encourage the development of methods with reproducible and robust training behavior, we propose a challenge paradigm where competitors are evaluated directly on the performance of their learning procedures rather than pre-trained agents.
Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning
To facilitate research in the direction of sample efficient reinforcement learning, we held the MineRL Competition on Sample Efficient Reinforcement Learning Using Human Priors at the Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019).
Planning with Abstract Learned Models While Learning Transferable Subtasks
We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
To that end, we introduce: (1) the Minecraft ObtainDiamond task, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods; and (2) the MineRL-v0 dataset, a large-scale collection of over 60 million state-action pairs of human demonstrations that can be resimulated into embodied trajectories with arbitrary modifications to game state and visuals.
