25 citations · 39 across the 9 of their papers we have counts for
6 papers · 1 filter
NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic Scenarios
Songyi Gao, Zuolin Tu, Rong-Jun Qin +3
Offline reinforcement learning (RL) aims to learn from historical data without requiring (costly) access to the environment. To facilitate offline RL research, we previously introd…
Unified Policy Optimization for Continuous-action Reinforcement Learning in Non-stationary Tasks and Games
Rong-Jun Qin, Fan-Ming Luo, Hong Qian +1
This paper addresses policy learning in non-stationary environments and games with continuous actions. Rather than the classical reward maximization mechanism, inspired by the idea…
Adversarial Counterfactual Environment Model Learning
Xiong-Hui Chen, Yang Yu, Zheng-Mao Zhu +8
A good model for action-effect prediction, named environment model, is important to achieve sample-efficient decision-making policy learning in many domains like robot control, rec…
Transferable Reward Learning by Dynamics-Agnostic Discriminator Ensemble
Fan-Ming Luo, Xingchen Cao, Rong-Jun Qin +1
Recovering reward function from expert demonstrations is a fundamental problem in reinforcement learning. The recovered reward function captures the motivation of the expert. Agent…
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning
Rongjun Qin, Songyi Gao, Xingyuan Zhang +5
Offline reinforcement learning (RL) aims at learning a good policy from a batch of collected data, without extra interactions with the environment during training. However, current…
Improving Fictitious Play Reinforcement Learning with Expanding Models
Rong-Jun Qin, Jing-Cheng Pang, Yang Yu
Fictitious play with reinforcement learning is a general and effective framework for zero-sum games. However, using the current deep neural network models, the implementation of fi…