105 citations · 242 across the 26 of their papers we have counts for
7 papers · 1 filter
SDMuse: Stochastic Differential Music Editing and Generation via Hybrid Representation
Chen Zhang, Yi Ren, Kejun Zhang +1
While deep generative models have empowered music generation, it remains a challenging and under-explored problem to edit an existing musical piece at fine granularity. In this pap…
RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning
Wei Qiu, Xiao Ma, Bo An +3
Despite the recent advancement in multi-agent reinforcement learning (MARL), the MARL agents easily overfit the training environment and perform poorly in the evaluation scenarios…
Boosting Offline Reinforcement Learning via Data Rebalancing
Yang Yue, Bingyi Kang, Xiao Ma +3
Offline reinforcement learning (RL) is challenged by the distributional shift between learning policies and datasets. To address this problem, existing works mainly focus on design…
Mutual Information Regularized Offline Reinforcement Learning
Xiao Ma, Bingyi Kang, Zhongwen Xu +2
The major challenge of offline RL is the distribution shift that appears when out-of-distribution actions are queried, which makes the policy improvement direction biased by extrap…
Efficient Offline Policy Optimization with a Learned Model
Zichen Liu, Siyi Li, Wee Sun Lee +2
MuZero Unplugged presents a promising approach for offline policy learning from logged data. It conducts Monte-Carlo Tree Search (MCTS) with a learned model and leverages Reanalyze…
Spikformer: When Spiking Neural Network Meets Transformer
Zhaokun Zhou, Yuesheng Zhu, Chao He +4
We consider two biologically plausible structures, the Spiking Neural Network (SNN) and the self-attention mechanism. The former offers an energy-efficient and event-driven paradig…
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine
Jiayi Weng, Min Lin, Shengyi Huang +9
There has been significant progress in developing reinforcement learning (RL) training systems. Past works such as IMPALA, Apex, Seed RL, Sample Factory, and others, aim to improve…