57 citations · 130 across the 9 of their papers we have counts for
9 papers
Robust Quadrupedal Locomotion via Risk-Averse Policy Learning
Jiyuan Shi, Chenjia Bai, Haoran He +6
The robustness of legged locomotion is crucial for quadrupedal robots in challenging terrains. Recently, Reinforcement Learning (RL) has shown promising results in legged locomotio…
Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models
Lei Han, Qingxu Zhu, Jiapeng Sheng +16
Knowledge from animals and humans inspires robotic innovations. Numerous efforts have been made to achieve agile locomotion in quadrupedal robots through classical controllers or r…
Optimistic Curiosity Exploration and Conservative Exploitation with Linear Reward Shaping
Hao Sun, Lei Han, Rui Yang +3
In this work, we study the simple yet universally applicable case of reward shaping in value-based Deep Reinforcement Learning (DRL). We show that reward shifting in the form of th…
Relative Policy-Transition Optimization for Fast Policy Transfer
Jiawei Xu, Cheng Zhou, Yizheng Zhang +2
We consider the problem of policy transfer between two Markov Decision Processes (MDPs). We introduce a lemma based on existing theoretical results in reinforcement learning to mea…
RORL: Robust Offline Reinforcement Learning via Conservative Smoothing
Rui Yang, Chenjia Bai, Xiaoteng Ma +3
Offline reinforcement learning (RL) provides a promising direction to exploit massive amount of offline data for complex decision-making tasks. Due to the distribution shift issue,…
Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL
Rui Yang, Yiming Lu, Wenzhe Li +6
Solving goal-conditioned tasks with sparse rewards using self-supervised learning is promising because of its simplicity and stability over current reinforcement learning (RL) algo…
Dynamic Bottleneck for Robust Self-Supervised Exploration
Chenjia Bai, Lingxiao Wang, Lei Han +4
Exploration methods based on pseudo-count of transitions or curiosity of dynamics have achieved promising results in solving reinforcement learning with sparse rewards. However, su…
MHER: Model-based Hindsight Experience Replay
Rui Yang, Meng Fang, Lei Han +3
Solving multi-goal reinforcement learning (RL) problems with sparse rewards is generally challenging. Existing approaches have utilized goal relabeling on collected experiences to…
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning
Chenjia Bai, Peng Liu, Kaiyu Liu +3
Efficient exploration remains a challenging problem in reinforcement learning, especially for tasks where extrinsic rewards from environments are sparse or even totally disregarded…