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Found 7 papers, 2 papers with code
EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data
We have expanded the performance of EfficientZero to multiple domains, encompassing both continuous and discrete actions, as well as visual and low-dimensional inputs.
Foundation Reinforcement Learning: towards Embodied Generalist Agents with Foundation Prior Assistance
Our method tolerates the unavoidable noise in embodied foundation models.
Seer: Language Instructed Video Prediction with Latent Diffusion Models
Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals.
Real-time scheduling of renewable power systems through planning-based reinforcement learning
The well-trained scheduling agent significantly reduces renewable curtailment and load shedding, which are issues arising from traditional scheduling's reliance on inaccurate day-ahead forecasts.
Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions
This paper proposes the Virtual MCTS (V-MCTS), a variant of MCTS that spends more search time on harder states and less search time on simpler states adaptively.
Planning for Sample Efficient Imitation Learning
Inspired by the recent success of EfficientZero in RL, we propose EfficientImitate (EI), a planning-based imitation learning method that can achieve high in-environment sample efficiency and performance simultaneously.
Mastering Atari Games with Limited Data
Recently, there has been significant progress in sample efficient image-based RL algorithms; however, consistent human-level performance on the Atari game benchmark remains an elusive goal.
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