Tianshou: a Highly Modularized Deep Reinforcement Learning Library
arXiv:2107.14171
Abstract
In this paper, we present Tianshou, a highly modularized Python library for deep reinforcement learning (DRL) that uses PyTorch as its backend. Tianshou intends to be research-friendly by providing a flexible and reliable infrastructure of DRL algorithms. It supports online and offline training with more than 20 classic algorithms through a unified interface. To facilitate related research and prove Tianshou's reliability, we have released Tianshou's benchmark of MuJoCo environments, covering eight classic algorithms with state-of-the-art performance. We open-sourced Tianshou at https://github.com/thu-ml/tianshou/.
References in corpus (9)
- Playing Atari with Deep Reinforcement Learning
- Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
- High-Dimensional Continuous Control Using Generalized Advantage Estimation
- Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
- Dopamine: A Research Framework for Deep Reinforcement Learning
- Accelerated Methods for Deep Reinforcement Learning
- Efficient Parallel Methods for Deep Reinforcement Learning
- ChainerRL: A Deep Reinforcement Learning Library
- rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch
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- RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
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- Recurrent Off-policy Baselines for Memory-based Continuous Control
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