chainerrl
TensorFlow2.0-for-Deep-Reinforcement-Learning
| chainerrl | TensorFlow2.0-for-Deep-Reinforcement-Learning | |
|---|---|---|
| 3 | 1 | |
| 1,141 | 81 | |
| 0.0% | - | |
| 0.0 | 0.0 | |
| over 2 years ago | 8 months ago | |
| Python | Python | |
| MIT License | - |
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chainerrl
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Help with my PyTorch implementation of PPO
Code for https://arxiv.org/abs/1709.06560 found: https://github.com/chainer/chainerrl
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Any working Acer implementation for continuous action space?
I implemented my version of Acer that supports discrete action space. I need to add an extension that supports continuous action space. I've seen a couple of implementations here and here. The first doesn't work for PongNoFrameskip-v4 and the other doesn't work in macOS.
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Beginner attempting to implement Noisy DQN
I tried all the versions I found and in most of them the network couldn't even learn to set the sigma as 0 (or close). The only implementation where I actually got improvement was by changing the noise directly when calling the noisy layers in this git. I don't know if this is the correct way but it sure showed good results.
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?
TensorLayer - Deep Learning and Reinforcement Learning Library for Scientists and Engineers
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).
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
tensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
deep-q-learning - Minimal Deep Q Learning (DQN & DDQN) implementations in Keras
trax - Trax — Deep Learning with Clear Code and Speed
DeepLearning - Contains all my works, references for deep learning
Deep-Reinforcement-Learning-Hands-On - Hands-on Deep Reinforcement Learning, published by Packt
fundamentalRL - educational codebase demonstrating some of the most common RL algorithms
deepdrive - Deepdrive is a simulator that allows anyone with a PC to push the state-of-the-art in self-driving