SOTAVerified

MuJoCo

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 501–550 of 677 papers

TitleStatusHype
Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies—0
Decorrelated Double Q-learning—0
Deep exploration by novelty-pursuit with maximum state entropy—0
Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks—0
DeepSafeMPC: Deep Learning-Based Model Predictive Control for Safe Multi-Agent Reinforcement Learning—0
Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online—0
DEER: A Delay-Resilient Framework for Reinforcement Learning with Variable Delays—0
DEFENDER: DTW-Based Episode Filtering Using Demonstrations for Enhancing RL Safety—0
Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback—0
Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study—0
DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynamic Manipulation of Deformable Linear Objects—0
DIDA: Denoised Imitation Learning based on Domain Adaptation—0
Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction—0
DisTop: Discovering a Topological representation to learn diverse and rewarding skills—0
Distributional Decision Transformer for Hindsight Information Matching—0
Distributionally Robust Statistical Verification with Imprecise Neural Networks—0
Diverse Imitation Learning via Self-OrganizingGenerative Models—0
dm_control: Software and Tasks for Continuous Control—0
DMFC-GraspNet: Differentiable Multi-Fingered Robotic Grasp Generation in Cluttered Scenes—0
Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation—0
DropoutDAgger: A Bayesian Approach to Safe Imitation Learning—0
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization—0
Effects of sparse rewards of different magnitudes in the speed of learning of model-based actor critic methods—0
Efficient Diversity-based Experience Replay for Deep Reinforcement Learning—0
Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration—0
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling—0
ELSIM: End-to-end learning of reusable skills through intrinsic motivation—0
Enhanced DACER Algorithm with High Diffusion Efficiency—0
EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning—0
Entropy Augmented Reinforcement Learning—0
Episodic Reinforcement Learning with Expanded State-reward Space—0
Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-RL—0
Evaluating Robustness of Cooperative MARL—0
Evolutionary Strategy Guided Reinforcement Learning via MultiBuffer Communication—0
Evolving Rewards to Automate Reinforcement Learning—0
Expected Policy Gradients—0
A Tractable Inference Perspective of Offline RL—0
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator—0
Fast Adaptation to New Environments via Policy-Dynamics Value Functions—0
Fast Convergence of Softmax Policy Mirror Ascent—0
FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control—0
Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments—0
Fight fire with fire: countering bad shortcuts in imitation learning with good shortcuts—0
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming—0
Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization—0
First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation—0
Follow the Object: Curriculum Learning for Manipulation Tasks with Imagined Goals—0
Formal Language Constrained Markov Decision Processes—0
FP3O: Enabling Proximal Policy Optimization in Multi-Agent Cooperation with Parameter-Sharing Versatility—0
From proprioception to long-horizon planning in novel environments: A hierarchical RL model—0
Show:102550
← PrevPage 11 of 14Next →

No leaderboard results yet.