3 citations · 4 across the 15 of their papers we have counts for
13 papers · 1 filter
When Does Non-Uniform Replay Matter in Reinforcement Learning?
Michal Korniak, Mikołaj 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…
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…
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…
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…
Compute-Optimal Scaling for Value-Based Deep RL
Preston Fu, Oleh Rybkin, Zhiyuan Zhou +4
As models grow larger and training them becomes expensive, it becomes increasingly important to scale training recipes not just to larger models and more data, but to do so in a co…
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…
Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners
Michal Nauman, Marek Cygan, Carmelo Sferrazza +2
Recent advances in language modeling and vision stem from training large models on diverse, multi-task data. This paradigm has had limited impact in value-based reinforcement learn…
Value-Based Deep RL Scales Predictably
Oleh Rybkin, Michal Nauman, Preston Fu +4
Scaling data and compute is critical to the success of modern ML. However, scaling demands predictability: we want methods to not only perform well with more compute or data, but a…
Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control
Michal Nauman, Mateusz Ostaszewski, Krzysztof Jankowski +2
Sample efficiency in Reinforcement Learning (RL) has traditionally been driven by algorithmic enhancements. In this work, we demonstrate that scaling can also lead to substantial i…
A Case for Validation Buffer in Pessimistic Actor-Critic
Michal Nauman, Mateusz Ostaszewski, Marek Cygan
In this paper, we investigate the issue of error accumulation in critic networks updated via pessimistic temporal difference objectives. We show that the critic approximation error…
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning
Michal Nauman, Michał Bortkiewicz, Piotr Miłoś +3
Recent advancements in off-policy Reinforcement Learning (RL) have significantly improved sample efficiency, primarily due to the incorporation of various forms of regularization t…
On the Theory of Risk-Aware Agents: Bridging Actor-Critic and Economics
Michal Nauman, Marek Cygan
Risk-aware Reinforcement Learning (RL) algorithms like SAC and TD3 were shown empirically to outperform their risk-neutral counterparts in a variety of continuous-action tasks. How…
On Many-Actions Policy Gradient
Michal Nauman, Marek Cygan
We study the variance of stochastic policy gradients (SPGs) with many action samples per state. We derive a many-actions optimality condition, which determines when many-actions SP…