2 papers
cs.LG2025
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…
cs.LG2024
Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning Rate
Fan-Ming Luo, Zuolin Tu, Zefang Huang +1
Real-world decision-making tasks are usually partially observable Markov decision processes (POMDPs), where the state is not fully observable. Recent progress has demonstrated that…