23 citations · 57 across the 11 of their papers we have counts for
12 papers
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…
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization
Hao Sun, Boris van Breugel, Jonathan Crabbe +2
Uncertainty Quantification (UQ) is essential for creating trustworthy machine learning models. Recent years have seen a steep rise in UQ methods that can flag suspicious examples,…
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…
Toward Causal-Aware RL: State-Wise Action-Refined Temporal Difference
Hao Sun, Taiyi Wang
Although it is well known that exploration plays a key role in Reinforcement Learning (RL), prevailing exploration strategies for continuous control tasks in RL are mainly based on…
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…