1 citations · 1 across the 7 of their papers we have counts for
8 papers
Drift Q-Learning
Anas Houssaini, Mohamad H. Danesh, Amin Abyaneh +3
Offline reinforcement learning requires improving a policy from fixed data while avoiding out-of-distribution actions with unreliable value estimates. Diffusion and flow policies h…
CA2: Code-Aware Agent for Automated Game Testing
Valliappan Chidambaram Adaikkappan, Vincent Martineau, Joshua Romoff +1
Automated game testing is important for verifying game functionality, but it remains a costly and time-consuming process. Manual testing often misses edge cases, and current automa…
MSPR: Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning
Valliappan Chidambaram Adaikkappan, David Meger, Sai Rajeswar +1
This paper investigates robust representation learning in offline goal-conditioned reinforcement learning (GCRL). Particularly in sparse reward scenarios, learning representations…
Tactile Modality Fusion for Vision-Language-Action Models
Charlotte Morissette, Amin Abyaneh, Wei-Di Chang +5
We propose TacFiLM, a lightweight modality-fusion approach that integrates visual-tactile signals into vision-language-action (VLA) models. While advances in VLAs have introduced r…
Multi-Agent Model-Based Reinforcement Learning with Joint State-Action Learned Embeddings
Zhizun Wang, David Meger
Learning to coordinate many agents in partially observable and highly dynamic environments requires both informative representations and data-efficient training. To address this ch…
Contractive Diffusion Policies: Robust Action Diffusion via Contractive Score-Based Sampling with Differential Equations
Amin Abyaneh, Charlotte Morissette, Mohamad H. Danesh +4
Diffusion policies have emerged as powerful generative models for offline policy learning, whose sampling process can be rigorously characterized by a score function guiding a stoc…
VOCALoco: Viability-Optimized Cost-aware Adaptive Locomotion
Stanley Wu, Mohamad H. Danesh, Simon Li +5
Recent advancements in legged robot locomotion have facilitated traversal over increasingly complex terrains. Despite this progress, many existing approaches rely on end-to-end dee…
Efficient Epistemic Uncertainty Estimation in Regression Ensemble Models Using Pairwise-Distance Estimators
Lucas Berry, David Meger
This work introduces an efficient novel approach for epistemic uncertainty estimation for ensemble models for regression tasks using pairwise-distance estimators (PaiDEs). Utilizin…