Deep-Reinforcement-Learning-Hands-On
TensorFlow2.0-for-Deep-Reinforcement-Learning
| Deep-Reinforcement-Learning-Hands-On | TensorFlow2.0-for-Deep-Reinforcement-Learning | |
|---|---|---|
| 2 | 1 | |
| 2,746 | 81 | |
| 0.0% | - | |
| 0.0 | 0.0 | |
| about 1 year ago | 8 months ago | |
| Python | Python | |
| MIT License | - |
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Deep-Reinforcement-Learning-Hands-On
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A2C/PPO with continuous action space
In some methods, like the one here, the actor network has two heads, one for the mean and one for the variance. In other methods, like the one here, the network only outputs the mean, while the variance is pre-defined and is decaying throughout the training.
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Beginner attempting to implement Noisy DQN
https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On/blob/baa9d013596ea8ea8ed6826b9de6679d98b897ca/Chapter07/lib/dqn_model.py#L9
TensorFlow2.0-for-Deep-Reinforcement-Learning
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Beginner attempting to implement Noisy DQN
I forgot to say that I'm using tensorflow, nevertheless I managed to find a git implementation for tensorflow 2 of the noisy dense layer (https://github.com/Huixxi/TensorFlow2.0-for-Deep-Reinforcement-Learning/blob/master/07_noisynet.py) and tried to adapt it to my needs.
What are some alternatives?
PPO-PyTorch - Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
pytorch-a2c-ppo-acktr-gail - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
trax - Trax — Deep Learning with Clear Code and Speed
chainerrl - ChainerRL is a deep reinforcement learning library built on top of Chainer.
deepdrive - Deepdrive is a simulator that allows anyone with a PC to push the state-of-the-art in self-driving