tianshou
Deep-Reinforcement-Learning-Algorithms-with-PyTorch
| tianshou | Deep-Reinforcement-Learning-Algorithms-with-PyTorch | |
|---|---|---|
| 8 | 2 | |
| 7,406 | 5,416 | |
| 1.3% | - | |
| 9.5 | 3.6 | |
| 6 days ago | 8 months ago | |
| Python | Python | |
| MIT License | MIT License |
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tianshou
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Is it better to not use the Target Update Frequency in Double DQN or depends on the application?
The tianshou implementation I found at https://github.com/thu-ml/tianshou/blob/master/tianshou/policy/modelfree/dqn.py is DQN by default.
- 他們能回來嗎
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Multi-Agent Stable Baselines
https://github.com/thu-ml/tianshou Imho there isn't a library that has it all, RLlib is quite good too, but I think that Tianshou is more similar to Pytorch and that helps to change the internals more intuitively and know what you are doing.
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Question about the old policy and new policy in TRPO code
Good point...I'll check in more detail when I get a chance later today! I would suggest looking at a more recent implementation like https://github.com/DLR-RM/stable-baselines3 or https://github.com/thu-ml/tianshou if you're trying to build. https://spinningup.openai.com/en/latest/algorithms/trpo.html is particularly good for understanding
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Tensorflow vs PyTorch for A3C
Do you absolutely need A3C? A2C has become more widely used (see, e.g., the comment in https://github.com/ikostrikov/pytorch-a3c, and the fact that both https://github.com/thu-ml/tianshou and https://github.com/facebookresearch/salina have A2C implementations, but no A3C at first glance).
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"Tianshou: a Highly Modularized Deep Reinforcement Learning Library", Weng et al 2021 (Python PyTorch MuJuCo; PPO, DQN, A2C, DDPG, SAC, TD3, REINFORCE, NPG, TRPO, ACKTR)
Code for https://arxiv.org/abs/2107.14171 found: https://github.com/thu-ml/tianshou/
Get the code for Tianshou here (GitHub).
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Best PyTorch RL library for doing research
I tried tianshou and thought it was well-designed for modularity, but it was early in development when I tried and missing some basic features
Deep-Reinforcement-Learning-Algorithms-with-PyTorch
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Frustrated beginner: How to approach/practice implementing papers into code?
This series is actually easier than the David Silver one. Also here is the github repository link - https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch
- Best PyTorch RL library for doing research
What are some alternatives?
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
pytorch-learn-reinforcement-learning - A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.
Deep-Q-Learning - Tensorflow implementation of Deepminds dqn with double dueling networks
seed_rl - SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.
mtrl - Multi Task RL Baselines
pytorch-a3c - PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning".
mbrl-lib - Library for Model Based RL