5 papers
FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control
Donghu Kim, Youngdo Lee, Minho Park +10
Reinforcement learning (RL) is a core approach for robot control when expert demonstrations are unavailable. On-policy methods such as Proximal Policy Optimization (PPO) are widely…
XQCfD: Accelerating Fast Actor-Critic Algorithms with Prior Data and Prior Policies
Daniel Palenicek, Florian Vogt, Joe Watson +3
For reinforcement learning in the real world online exploration is expensive A common practice in robotic reinforcement learning is to incorporate additional data to improve sample…
XQC: Well-conditioned Optimization Accelerates Deep Reinforcement Learning
Daniel Palenicek, Florian Vogt, Joe Watson +2
Sample efficiency is a central property of effective deep reinforcement learning algorithms. Recent work has improved this through added complexity, such as larger models, exotic n…
Scaling CrossQ with Weight Normalization
Daniel Palenicek, Florian Vogt, Jan Peters
Reinforcement learning has achieved significant milestones, but sample efficiency remains a bottleneck for real-world applications. Recently, CrossQ has demonstrated state-of-the-a…
Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization
Daniel Palenicek, Florian Vogt, Joe Watson +1
Reinforcement learning has achieved significant milestones, but sample efficiency remains a bottleneck for real-world applications. Recently, CrossQ has demonstrated state-of-the-a…