11 papers
Reward-Conditioned Reinforcement Learning
Michal Nauman, Marek Cygan, Pieter Abbeel
Single-task RL agents are typically trained under a fixed reward function, which limits their robustness to reward misspecification and their ability to adapt to changing preferenc…
When Does Non-Uniform Replay Matter in Reinforcement Learning?
Michal Korniak, MikoÅaj Czarnecki, Yarden As +3
Modern off-policy reinforcement learning algorithms often rely on simple uniform replay sampling and it remains unclear when and why non-uniform replay improves over this strong ba…
What Does Flow Matching Bring To TD Learning?
Bhavya Agrawalla, Michal Nauman, Aviral Kumar
Recent work shows that flow matching can be effective for scalar Q-value function estimation in reinforcement learning (RL), but it remains unclear why or how this approach differs…
Relative Entropy Pathwise Policy Optimization
Claas Voelcker, Axel Brunnbauer, Marcel Hussing +6
Score-function based methods for policy learning, such as REINFORCE and PPO, have delivered strong results in game-playing and robotics, yet their high variance often undermines tr…
Beyond Recognition: Evaluating Visual Perspective Taking in Vision Language Models
Gracjan Góral, Alicja Ziarko, Piotr MiÅoÅ +3
We investigate the ability of Vision Language Models (VLMs) to perform visual perspective taking using a new set of visual tasks inspired by established human tests. Our approach l…
floq: Training Critics via Flow-Matching for Scaling Compute in Value-Based RL
Bhavya Agrawalla, Michal Nauman, Khush Agrawal +1
A hallmark of modern large-scale machine learning techniques is the use of training objectives that provide dense supervision to intermediate computations, such as teacher forcing…