Merel, Josh

Emergence of Locomotion Behaviours in Rich Environments

Heess, N., TB, D., Sriram, S., Lemmon, J., Merel, J., Wayne, G., Tassa, Y., Erez, T., Wang, Z., Eslami, S. M. A., et al. (2017) · arXiv

Reinforcement Learning in RoboticsRobotic Locomotion and Control

Cited by 657

DeepMind Control Suite

Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., Casas, D. D. L., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., et al. (2018) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 497

Graph networks as learnable physics engines for inference and control

Sanchez-Gonzalez, A., Heess, N., Springenberg, J. T., Merel, J., Riedmiller, M., Hadsell, R., & Battaglia, P. (2018) · arXiv

Reinforcement Learning in RoboticsModel Reduction and Neural Networks

Cited by 387

Hierarchical motor control in mammals and machines

Merel, J., Botvinick, M., & Wayne, G. (2019) · Nature Communications

Reinforcement Learning in RoboticsMotor Control and Adaptation

Cited by 274

Critic regularized regression

Wang, Z., Novikov, A., Zolna, K., Springenberg, J. T., Reed, S., Shahriari, B., Siegel, N., Merel, J., Gulcehre, C., Heess, N., et al. (2020) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 128

From motor control to team play in simulated humanoid football

Liu, S., Lever, G., Wang, Z., Merel, J., Eslami, S. M. A., Hennes, D., Czarnecki, W. M., Tassa, Y., Omidshafiei, S., Abdolmaleki, A., et al. (2022) · Science Robotics

Reinforcement Learning in RoboticsHuman Motion and Animation

Cited by 88

A virtual rodent predicts the structure of neural activity across behaviours

Aldarondo, D., Merel, J., Marshall, J. D., Hasenclever, L., Klibaite, U., Gellis, A., Tassa, Y., Wayne, G., Botvinick, M., & Ölveczky, B. P. (2024) · Nature

Motor Control and AdaptationNeural dynamics and brain function

Cited by 70

Deep neuroethology of a virtual rodent

Merel, J., Aldarondo, D., Marshall, J., Tassa, Y., Wayne, G., & Ölveczky, B. (2019) · arXiv

Reinforcement Learning in RoboticsMotor Control and Adaptation

Cited by 44

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

Gulcehre, C., Wang, Z., Novikov, A., Paine, T. L., Colmenarejo, S. G., Zolna, K., Agarwal, R., Merel, J., Mankowitz, D., Paduraru, C., et al. (2020) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 42

Sensing flow gradients is necessary for learning autonomous underwater navigation

Jiao, Y., Hang, H., Merel, J., & Kanso, E. (2025) · Nature Communications

Underwater Vehicles and Communication SystemsReinforcement Learning in Robotics

Cited by 23

Universal humanoid motion representations for physics-based control

Luo, Z., Cao, J., Merel, J., Winkler, A., Huang, J., Kitani, K., & Xu, W. (2023) · arXiv

Human Motion and AnimationReinforcement Learning in Robotics

Cited by 10

Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning

Lindsay, G. W., Merel, J., Mrsic-Flogel, T., & Sahani, M. (2021) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 4