Search Results for author: Hongyi Zhou

Found 5 papers, 2 papers with code

Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning

no code implementations21 Jan 2024 Ge Li, Hongyi Zhou, Dominik Roth, Serge Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann

Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state.

Reinforcement Learning (RL)

MP3: Movement Primitive-Based (Re-)Planning Policy

no code implementations22 Jun 2023 Fabian Otto, Hongyi Zhou, Onur Celik, Ge Li, Rudolf Lioutikov, Gerhard Neumann

We introduce a novel deep reinforcement learning (RL) approach called Movement Primitive-based Planning Policy (MP3).

Reinforcement Learning (RL)

Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method

1 code implementation15 Oct 2020 Zuxin Liu, Hongyi Zhou, Baiming Chen, Sicheng Zhong, Martial Hebert, Ding Zhao

We propose a model-based approach to enable RL agents to effectively explore the environment with unknown system dynamics and environment constraints given a significantly small number of violation budgets.

Model-based Reinforcement Learning Model Predictive Control +3

MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments

no code implementations30 Jul 2020 Zuxin Liu, Baiming Chen, Hongyi Zhou, Guru Koushik, Martial Hebert, Ding Zhao

Multi-agent navigation in dynamic environments is of great industrial value when deploying a large scale fleet of robot to real-world applications.

reinforcement-learning Reinforcement Learning (RL)

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