385 citations · 959 across the 32 of their papers we have counts for
7 papers · 1 filter
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…
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization
Quanyi Li, Zhenghao Peng, Bolei Zhou
Human intervention is an effective way to inject human knowledge into the training loop of reinforcement learning, which can bring fast learning and ensured training safety. Given…
Safe Exploration by Solving Early Terminated MDP
Hao Sun, Ziping Xu, Meng Fang +4
Safe exploration is crucial for the real-world application of reinforcement learning (RL). Previous works consider the safe exploration problem as Constrained Markov Decision Proce…
Non-local Policy Optimization via Diversity-regularized Collaborative Exploration
Zhenghao Peng, Hao Sun, Bolei Zhou
Conventional Reinforcement Learning (RL) algorithms usually have one single agent learning to solve the task independently. As a result, the agent can only explore a limited part o…
Evolutionary Stochastic Policy Distillation
Hao Sun, Xinyu Pan, Bo Dai +2
Solving the Goal-Conditioned Reward Sparse (GCRS) task is a challenging reinforcement learning problem due to the sparsity of reward signals. In this work, we propose a new formula…
Policy Continuation with Hindsight Inverse Dynamics
Hao Sun, Zhizhong Li, Xiaotong Liu +2
Solving goal-oriented tasks is an important but challenging problem in reinforcement learning (RL). For such tasks, the rewards are often sparse, making it difficult to learn a pol…