Search Results for author:
Found 6 papers, 4 papers with code
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Our policy gradient results allow for leveraging approximate symmetries of the environment for policy optimization.
Hypernetworks for Zero-shot Transfer in Reinforcement Learning
In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal RL solutions for training tasks.
Continuous MDP Homomorphisms and Homomorphic Policy Gradient
Abstraction has been widely studied as a way to improve the efficiency and generalization of reinforcement learning algorithms.
Learning Intuitive Physics with Multimodal Generative Models
Predicting the future interaction of objects when they come into contact with their environment is key for autonomous agents to take intelligent and anticipatory actions.
Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference
Utilization of latent space to capture a lower-dimensional representation of a complex dynamics model is explored in this work.
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning
Motivated by the recursive Newton-Euler formulation, we propose a novel cascaded Gaussian process learning framework for the inverse dynamics of robot manipulators.
