23 citations · 103 across the 55 of their papers we have counts for
6 papers · 1 filter
RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences
Logan Mondal Bhamidipaty, Mykel Kochenderfer, Subramanian Ramamoorthy
World models are widely used in offline reinforcement learning (RL) to improve sample efficiency and generate experience beyond a fixed dataset. However, they are vulnerable to mod…
Robust Learning from Observation with Model Misspecification
Luca Viano, Yu-Ting Huang, Parameswaran Kamalaruban +3
Imitation learning (IL) is a popular paradigm for training policies in robotic systems when specifying the reward function is difficult. However, despite the success of IL algorith…
Elaborating on Learned Demonstrations with Temporal Logic Specifications
Craig Innes, Subramanian Ramamoorthy
Most current methods for learning from demonstrations assume that those demonstrations alone are sufficient to learn the underlying task. This is often untrue, especially if extra…
Learning Structured Representations of Spatial and Interactive Dynamics for Trajectory Prediction in Crowded Scenes
Todor Davchev, Michael Burke, Subramanian Ramamoorthy
Context plays a significant role in the generation of motion for dynamic agents in interactive environments. This work proposes a modular method that utilises a learned model of th…
Iterative Model-Based Reinforcement Learning Using Simulations in the Differentiable Neural Computer
Adeel Mufti, Svetlin Penkov, Subramanian Ramamoorthy
We propose a lifelong learning architecture, the Neural Computer Agent (NCA), where a Reinforcement Learning agent is paired with a predictive model of the environment learned by a…
Learning Programmatically Structured Representations with Perceptor Gradients
Svetlin Penkov, Subramanian Ramamoorthy
We present the perceptor gradients algorithm -- a novel approach to learning symbolic representations based on the idea of decomposing an agent's policy into i) a perceptor network…