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Found 17 papers, 4 papers with code
Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft
In this work, we present the solution that won first place and was awarded the most human-like agent in the 2021 NeurIPS Competition MineRL BASALT Challenge: Learning from Human Feedback in Minecraft, which challenged participants to use human data to solve four tasks defined only by a natural language description and no reward function.
Cycle-of-Learning for Autonomous Systems from Human Interaction
We discuss different types of human-robot interaction paradigms in the context of training end-to-end reinforcement learning algorithms.
Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time
This paper investigates how to utilize different forms of human interaction to safely train autonomous systems in real-time by learning from both human demonstrations and interventions.
Imitation Learning with Human Eye Gaze via Multi-Objective Prediction
In this work, we propose Gaze Regularized Imitation Learning (GRIL), a novel context-aware, imitation learning architecture that learns concurrently from both human demonstrations and eye gaze to solve tasks where visual attention provides important context.
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments
However, it is currently unclear how to efficiently update that policy using reinforcement learning as these approaches are inherently optimizing different objective functions.
PODNet: A Neural Network for Discovery of Plannable Options
Learning from demonstration has been widely studied in machine learning but becomes challenging when the demonstrated trajectories are unstructured and follow different objectives.
Combining Visible and Infrared Spectrum Imagery using Machine Learning for Small Unmanned Aerial System Detection
However, compared to widely available visible spectrum sensors, LWIR sensors have lower resolution and may produce more false positives when exposed to birds or other heat sources.
Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems
This can be attributed to the fact that current state-of-the-art, end-to-end reinforcement learning approaches still require thousands or millions of data samples to converge to a satisfactory policy and are subject to catastrophic failures during training.
On games and simulators as a platform for development of artificial intelligence for command and control
Games and simulators can be a valuable platform to execute complex multi-agent, multiplayer, imperfect information scenarios with significant parallels to military applications: multiple participants manage resources and make decisions that command assets to secure specific areas of a map or neutralize opposing forces.
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.
Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II
Traditionally, learning from human demonstrations via direct behavior cloning can lead to high-performance policies given that the algorithm has access to large amounts of high-quality data covering the most likely scenarios to be encountered when the agent is operating.
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.
DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarios
The development of plans of action in disaster response scenarios is a time-consuming process.
DIP-RL: Demonstration-Inferred Preference Learning in Minecraft
In machine learning for sequential decision-making, an algorithmic agent learns to interact with an environment while receiving feedback in the form of a reward signal.
COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations
The development of Courses of Action (COAs) in military operations is traditionally a time-consuming and intricate process.
Scalable Interactive Machine Learning for Future Command and Control
Future warfare will require Command and Control (C2) personnel to make decisions at shrinking timescales in complex and potentially ill-defined situations.
Re-Envisioning Command and Control
Future warfare will require Command and Control (C2) decision-making to occur in more complex, fast-paced, ill-structured, and demanding conditions.
