pytorch-A3C
pytorch-a3c
pytorch-A3C | pytorch-a3c | |
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3 | 1 | |
568 | 1,170 | |
- | - | |
0.0 | 10.0 | |
about 1 year ago | over 4 years ago | |
Python | Python | |
MIT License | MIT License |
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pytorch-A3C
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Formula to compute loss in A3C
I'm a beginner to RL and I'm trying to understand how the loss function was computed. If it follows a specific formular. I've read the a3c algorithm overview on paper by barto but it seems the implemtation here https://github.com/MorvanZhou/pytorch-A3C/blob/master/discrete_A3C.py is different.
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How to measure the performance of a3c algorithm
I'm new to RL and i just started going through this implementation of a3c https://github.com/MorvanZhou/pytorch-A3C
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Tensorflow vs PyTorch for A3C
For the A3C part, I would appreciate your insights on whether to use Tensorflow or PyTorch to implement the algorithm. This GitHub https://github.com/MorvanZhou/pytorch-A3C tries to explain some things but it still isn't very clear to me which is the best, as I see that many implementations with TensorFlow. So if you have anything to add to help me choose one framework, I would very thankful.
pytorch-a3c
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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).
What are some alternatives?
salina - a Lightweight library for sequential learning agents, including reinforcement learning
tianshou - An elegant PyTorch deep reinforcement learning library.
Muzero-unplugged - Pytorch Implementation of MuZero Unplugged for gym environment. This algorithm is capable of supporting a wide range of action and observation spaces, including both discrete and continuous variations.
Note - Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, Gemma, CLIP, ViT, ConvNeXt, Segformer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
muzero-general - MuZero