2 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization
Liam Schramm, Abdeslam Boularias
Monte Carlo tree search (MCTS) has been successful in a variety of domains, but faces challenges with long-horizon exploration when compared to sampling-based motion planning algor…
Bellman Diffusion Models
Liam Schramm, Abdeslam Boularias
Diffusion models have seen tremendous success as generative architectures. Recently, they have been shown to be effective at modelling policies for offline reinforcement learning a…
USHER: Unbiased Sampling for Hindsight Experience Replay
Liam Schramm, Yunfu Deng, Edgar Granados +1
Dealing with sparse rewards is a long-standing challenge in reinforcement learning (RL). Hindsight Experience Replay (HER) addresses this problem by reusing failed trajectories for…
Learning to Transfer Dynamic Models of Underactuated Soft Robotic Hands
Liam Schramm, Avishai Sintov, Abdeslam Boularias
Transfer learning is a popular approach to bypassing data limitations in one domain by leveraging data from another domain. This is especially useful in robotics, as it allows prac…