DI-drive
tianshou
| DI-drive | tianshou | |
|---|---|---|
| 2 | 8 | |
| 521 | 7,459 | |
| -16.1% | 2.0% | |
| 0.0 | 9.5 | |
| over 1 year ago | 6 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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DI-drive
- Try simple interfaces and customized driving policy and casezoo set on DI-drive！
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Is reinforcement learning being used for the development of self-driving cars?
Some attempts on driving simulators have achieved good results(eg. DI-drive, DI-drive is an open-source application platform under OpenDILab. DI-drive applies different simulator/datasets/cases in Decision Intelligence Training & Testing for Autonomous Driving Policy). The basic idea mainly includes initializing with imitation learning, and then using reinforcement learning to obtain results that surpass expert data after reaching a certain performance. Some use the perceptual Label to train the backbone of the network, then freeze the backbone, and use reinforcement learning to specifically train the affordance method from perceptual embedding to action output. Others use a multi-model fusion approach, in which the model trained by reinforcement learning is used together with other methods to obtain the driving output. However, the emulator-based method is mainly end-to-end, and its security is difficult to guarantee, and it is difficult to apply to real vehicle scenarios.
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
What are some alternatives?
mini-AlphaStar - (JAIR'2022) A mini-scale reproduction code of the AlphaStar program. Note: the original AlphaStar is the AI proposed by DeepMind to play StarCraft II. JAIR = Journal of Artificial Intelligence Research.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
imitation - Clean PyTorch implementations of imitation and reward learning algorithms
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
neat - [ICCV'21] NEAT: Neural Attention Fields for End-to-End Autonomous Driving
ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥
eirli - An Empirical Investigation of Representation Learning for Imitation (EIRLI), NeurIPS'21
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.
seed_rl - SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.
pytorch-a3c - PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning".
Deep-Reinforcement-Learning-Algorithms-with-PyTorch - PyTorch implementations of deep reinforcement learning algorithms and environments
pomdp-baselines - Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022