LLM Augmented Hierarchical Agents

1 code implementation • 9 Nov 2023 • Bharat Prakash, Tim Oates, Tinoosh Mohsenin

However, using LLMs to solve real world problems is hard because they are not grounded in the current task.

In-Context Learning Reinforcement Learning (RL)

ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents

no code implementations • 17 Aug 2023 • Tejaswini Manjunath, Mozhgan Navardi, Prakhar Dixit, Bharat Prakash, Tinoosh Mohsenin

In real-world environments with sparse rewards and multiple goals, learning is still a major challenge and Reinforcement Learning (RL) algorithms fail to learn good policies.

reinforcement-learning Reinforcement Learning (RL)

Towards an Interpretable Hierarchical Agent Framework using Semantic Goals

no code implementations • 16 Oct 2022 • Bharat Prakash, Nicholas Waytowich, Tim Oates, Tinoosh Mohsenin

Learning to solve long horizon temporally extended tasks with reinforcement learning has been a challenge for several years now.

reinforcement-learning Reinforcement Learning (RL)

Retrospective on the 2021 BASALT Competition on Learning from Human Feedback

no code implementations • 14 Apr 2022 • Rohin Shah, Steven H. Wang, Cody Wild, Stephanie Milani, Anssi Kanervisto, Vinicius G. Goecks, Nicholas Waytowich, David Watkins-Valls, Bharat Prakash, Edmund Mills, Divyansh Garg, Alexander Fries, Alexandra Souly, Chan Jun Shern, Daniel del Castillo, Tom Lieberum

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

1 code implementation • 7 Dec 2021 • Vinicius G. Goecks, Nicholas Waytowich, David Watkins-Valls, Bharat Prakash

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.

Imitation Learning

Automatic Goal Generation using Dynamical Distance Learning

no code implementations • 7 Nov 2021 • Bharat Prakash, Nicholas Waytowich, Tinoosh Mohsenin, Tim Oates

In this work, we propose a method for automatic goal generation using a dynamical distance function (DDF) in a self-supervised fashion.

Decision Making Reinforcement Learning (RL)

Interactive Hierarchical Guidance using Language

no code implementations • 9 Oct 2021 • Bharat Prakash, Nicholas Waytowich, Tim Oates, Tinoosh Mohsenin

The low-level controller executes the sub-tasks based on the language commands.

energy management Management

Neural Networks for Pulmonary Disease Diagnosis using Auditory and Demographic Information

no code implementations • 26 Nov 2020 • Morteza Hosseini, Haoran Ren, Hasib-Al Rashid, Arnab Neelim Mazumder, Bharat Prakash, Tinoosh Mohsenin

Pulmonary diseases impact millions of lives globally and annually.

Specificity

On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning

no code implementations • 25 Mar 2019 • Bharat Prakash, Mark Horton, {Nicholas R. Waytowich, William David Hairston, Tim Oates, Tinoosh Mohsenin

This compression model is vital to efficiently learn policies, especially when learning on embedded systems.

reinforcement-learning Reinforcement Learning (RL)

Improving Safety in Reinforcement Learning Using Model-Based Architectures and Human Intervention

no code implementations • 22 Mar 2019 • Bharat Prakash, Mohit Khatwani, Nicholas Waytowich, Tinoosh Mohsenin

Recent progress in AI and Reinforcement learning has shown great success in solving complex problems with high dimensional state spaces.

reinforcement-learning Reinforcement Learning (RL)