5 papers
Is Temporal Difference Learning the Gold Standard for Stitching in RL?
Michał Bortkiewicz, Władysław Pałucki, Mateusz Ostaszewski +1
Reinforcement learning (RL) promises to solve long-horizon tasks even when training data contains only short fragments of the behaviors. This experience stitching capability is oft…
Contrastive Representations for Temporal Reasoning
Alicja Ziarko, Michal Bortkiewicz, Michal Zawalski +2
In classical AI, perception relies on learning state-based representations, while planning, which can be thought of as temporal reasoning over action sequences, is typically achiev…
Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning
Rafał Surdej, Michał Bortkiewicz, Alex Lewandowski +2
Trainable activation functions, whose parameters are optimized alongside network weights, offer increased expressivity compared to fixed activation functions. Specifically, trainab…
1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
Kevin Wang, Ishaan Javali, Michał Bortkiewicz +2
Scaling up self-supervised learning has driven breakthroughs in language and vision, yet comparable progress has remained elusive in reinforcement learning (RL). In this paper, we…
Emergency action termination for immediate reaction in hierarchical reinforcement learning
Michał Bortkiewicz, Jakub Łyskawa, Paweł Wawrzyński +3
Hierarchical decomposition of control is unavoidable in large dynamical systems. In reinforcement learning (RL), it is usually solved with subgoals defined at higher policy levels…