Sample Efficient Ensemble Learning with Catalyst.RL

• URL: http://arxiv.org/abs/2003.14210v2
• Date: Tue, 7 Apr 2020 22:17:13 GMT
• Title: Sample Efficient Ensemble Learning with Catalyst.RL
• Authors: Sergey Kolesnikov and Valentin Khrulkov
• Abstract summary: We present Catalyst.RL, an open-source 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.
• Score: 13.726637149320272
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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