24 citations · 31 across the 3 of their papers we have counts for
3 papers
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned
Anssi Kanervisto, Stephanie Milani, Karolis Ramanauskas +19
Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem. To promote the development of more…
Generative Adversarial Exploration for Reinforcement Learning
Weijun Hong, Menghui Zhu, Minghuan Liu +4
Exploration is crucial for training the optimal reinforcement learning (RL) policy, where the key is to discriminate whether a state visiting is novel. Most previous work focuses o…
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search
Weijun Hong, Guilin Li, Weinan Zhang +4
Neural architecture search (NAS) has shown encouraging results in automating the architecture design. Recently, DARTS relaxes the search process with a differentiable formulation t…