Search Results for author:
Found 18 papers, 6 papers with code
Use-Case-Grounded Simulations for Explanation Evaluation
SimEvals involve training algorithmic agents that take as input the information content (such as model explanations) that would be presented to each participant in a human subject study, to predict answers to the use case of interest.
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
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.
A Survey of Explainable Reinforcement Learning
In this survey, we propose a novel taxonomy for organizing the XRL literature that prioritizes the RL setting.
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).
MineRL: A Large-Scale Dataset of Minecraft Demonstrations
Therefore, we introduce a comprehensive, large-scale, simulator-paired dataset of human demonstrations: MineRL.
Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy
There is a growing desire in the field of reinforcement learning (and machine learning in general) to move from black-box models toward more "interpretable AI."
Generation of Policy-Level Explanations for Reinforcement Learning
Though reinforcement learning has greatly benefited from the incorporation of neural networks, the inability to verify the correctness of such systems limits their use.
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.
Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates
In this paper, we show a phenomenon, which we named ``super-convergence'', where residual networks can be trained using an order of magnitude fewer iterations than is used with standard training methods.
Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates
One of the key elements of super-convergence is training with one learning rate cycle and a large maximum learning rate.
Exploring loss function topology with cyclical learning rates
We present observations and discussion of previously unreported phenomena discovered while training residual networks.
Deep Convolutional Neural Network Design Patterns
Recent research in the deep learning field has produced a plethora of new architectures.
