10 citations · 30 across the 9 of their papers we have counts for
9 papers
LLM Augmented Hierarchical Agents
Bharat Prakash, Tim Oates, Tinoosh Mohsenin
Solving long-horizon, temporally-extended tasks using Reinforcement Learning (RL) is challenging, compounded by the common practice of learning without prior knowledge (or tabula r…
ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents
Tejaswini Manjunath, Mozhgan Navardi, Prakhar Dixit +2
Robots have been successfully used to perform tasks with high precision. In real-world environments with sparse rewards and multiple goals, learning is still a major challenge and…
Towards an Interpretable Hierarchical Agent Framework using Semantic Goals
Bharat Prakash, Nicholas Waytowich, Tim Oates +1
Learning to solve long horizon temporally extended tasks with reinforcement learning has been a challenge for several years now. We believe that it is important to leverage both th…
Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft
Vinicius G. Goecks, Nicholas Waytowich, David Watkins-Valls +1
Real-world tasks of interest are generally poorly defined by human-readable descriptions and have no pre-defined reward signals unless it is defined by a human designer. Conversely…
Automatic Goal Generation using Dynamical Distance Learning
Bharat Prakash, Nicholas Waytowich, Tinoosh Mohsenin +1
Reinforcement Learning (RL) agents can learn to solve complex sequential decision making tasks by interacting with the environment. However, sample efficiency remains a major chall…
Interactive Hierarchical Guidance using Language
Bharat Prakash, Nicholas Waytowich, Tim Oates +1
Reinforcement learning has been successful in many tasks ranging from robotic control, games, energy management etc. In complex real world environments with sparse rewards and long…
Neural Networks for Pulmonary Disease Diagnosis using Auditory and Demographic Information
Morteza Hosseini, Haoran Ren, Hasib-Al Rashid +3
Pulmonary diseases impact millions of lives globally and annually. The recent outbreak of the pandemic of the COVID-19, a novel pulmonary infection, has more than ever brought the…
On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning
Bharat Prakash, Mark Horton, Nicholas R. Waytowich +3
In autonomous embedded systems, it is often vital to reduce the amount of actions taken in the real world and energy required to learn a policy. Training reinforcement learning age…
Improving Safety in Reinforcement Learning Using Model-Based Architectures and Human Intervention
Bharat Prakash, Mohit Khatwani, Nicholas Waytowich +1
Recent progress in AI and Reinforcement learning has shown great success in solving complex problems with high dimensional state spaces. However, most of these successes have been…