DeepMind Control
Emerging8papers using it
2024first seen
The 'DeepMind Control' benchmark is a suite of continuous control tasks used to evaluate the performance of reinforcement learning algorithms.
Papers using DeepMind Control (8)
- Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement LearningReflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous ControlGIRL: Generative Imagination Reinforcement Learning via Information-Theoretic Hallucination ControlWIMLE: Uncertainty-Aware World Models with IMLE for Sample-Efficient Continuous ControlRevisiting Bisimulation Metric for Robust Representations in Reinforcement LearningScaling CrossQ with Weight NormalizationState Chrono Representation for Enhancing Generalization in
Reinforcement LearningCertifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions