6 papers
On the Role of Computation in Reinforcement Learning
Raj Ghugare, MichaÅ Bortkiewicz, Alicja Ziarko +1
How does the amount of compute available to a reinforcement learning (RL) policy affect its learning? Can policies using a fixed amount of parameters, still benefit from additional…
1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
Kevin Wang, Ishaan Javali, MichaÅ 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…
Accelerating Goal-Conditioned RL Algorithms and Research
MichaÅ Bortkiewicz, WÅadysÅaw PaÅucki, Vivek Myers +4
Self-supervision has the potential to transform reinforcement learning (RL), paralleling the breakthroughs it has enabled in other areas of machine learning. While self-supervised…
Is Temporal Difference Learning the Gold Standard for Stitching in RL?
MichaÅ Bortkiewicz, WÅadysÅaw PaÅucki, 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
RafaÅ Surdej, MichaÅ Bortkiewicz, Alex Lewandowski +2
Trainable activation functions, whose parameters are optimized alongside network weights, offer increased expressivity compared to fixed activation functions. Specifically, trainab…