Search Results for author:
Found 19 papers, 2 papers with code
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.
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.
Rating-based Reinforcement Learning
This paper develops a novel rating-based reinforcement learning approach that uses human ratings to obtain human guidance in reinforcement learning.
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.
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.
Towards an Interpretable Hierarchical Agent Framework using Semantic Goals
Learning to solve long horizon temporally extended tasks with reinforcement learning has been a challenge for several years now.
Multiple View Performers for Shape Completion
We propose the Multiple View Performer (MVP) - a new architecture for 3D shape completion from a series of temporally sequential views.
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.
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.
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.
Automatic Goal Generation using Dynamical Distance Learning
In this work, we propose a method for automatic goal generation using a dynamical distance function (DDF) in a self-supervised fashion.
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.
Interactive Hierarchical Guidance using Language
The low-level controller executes the sub-tasks based on the language commands.
A Narration-based Reward Shaping Approach using Grounded Natural Language Commands
While this problem can be addressed through reward shaping, such approaches typically require a human expert with specialized knowledge.
Learning from Observations Using a Single Video Demonstration and Human Feedback
In this paper, we present a method for learning from video demonstrations by using human feedback to construct a mapping between the standard representation of the agent and the visual representation of the demonstration.
Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation
We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments.
Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning
While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniques are still susceptible to the longstanding problem of {\em reward sparsity}.
Improving Safety in Reinforcement Learning Using Model-Based Architectures and Human Intervention
Recent progress in AI and Reinforcement learning has shown great success in solving complex problems with high dimensional state spaces.
Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces
While recent advances in deep reinforcement learning have allowed autonomous learning agents to succeed at a variety of complex tasks, existing algorithms generally require a lot of training data.
