Towards closing the sim-to-real gap in collaborative multi-robot deep reinforcement learning

W Zhao, JP Queralta, L Qingqing… - 2020 5th International …, 2020 - ieeexplore.ieee.org
Current research directions in deep reinforcement learning include bridging the simulation-
reality gap, improving sample efficiency of experiences in distributed multi-agent …
被引用次数：32 相关文章

Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement Learning

W Zhao, J Peña Queralta, L Qingqing… - arXiv e …, 2020 - ui.adsabs.harvard.edu
Current research directions in deep reinforcement learning include bridging the simulation-
reality gap, improving sample efficiency of experiences in distributed multi-agent …
[PDF] utupub.fi

[PDF][PDF] Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep

W Zhao, JP Queralta, L Qingqing, T Westerlund - 2021 - utupub.fi
Current research directions in deep reinforcement learning include bridging the simulation-
reality gap, improving sample efficiency of experiences in distributed multi-agent …

Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement Learning

W Zhao, JP Queralta, L Qingqing… - arXiv preprint arXiv …, 2020 - arxiv.org
Current research directions in deep reinforcement learning include bridging the simulation-
reality gap, improving sample efficiency of experiences in distributed multi-agent …
[PDF] researchgate.net

[PDF][PDF] Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement Learning

W Zhao, JP Queralta, L Qingqing, T Westerlund - researchgate.net
Current research directions in deep reinforcement learning include bridging the simulation-
reality gap, improving sample efficiency of experiences in distributed multi-agent …
[PDF] researchgate.net

[PDF][PDF] Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement Learning

W Zhao, JP Queralta, L Qingqing, T Westerlund - researchgate.net
Current research directions in deep reinforcement learning include bridging the simulation-
reality gap, improving sample efficiency of experiences in distributed multi-agent …