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• W3014538265 abstract "We present Catalyst.RL, an open-source PyTorch framework for reproducible and sample efficient reinforcement learning (RL) research. Main features of Catalyst.RL include large-scale asynchronous distributed training, efficient implementations of various RL algorithms and auxiliary tricks, such as n-step returns, value distributions, hyperbolic reinforcement learning, etc. To demonstrate the effectiveness of Catalyst.RL, we applied it to a physics-based reinforcement learning challenge NeurIPS 2019: Learn to Move -- Walk Around with the objective to build a locomotion controller for a human musculoskeletal model. The environment is computationally expensive, has a high-dimensional continuous action space and is stochastic. Our team took the 2nd place, capitalizing on the ability of Catalyst.RL to train high-quality and sample-efficient RL agents in only a few hours of training time. The implementation along with experiments is open-sourced so results can be reproduced and novel ideas tried out." @default.
• W3014538265 created "2020-04-10" @default.
• W3014538265 creator A5040956144 @default.
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• W3014538265 date "2020-03-29" @default.
• W3014538265 modified "2023-09-27" @default.
• W3014538265 title "Sample Efficient Ensemble Learning with Catalyst.RL." @default.
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