DeepLearning
chainerrl
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| DeepLearning | chainerrl | |
|---|---|---|
| 1 | 3 | |
| 3 | 1,141 | |
| - | 0.0% | |
| 0.0 | 0.0 | |
| almost 2 years ago | over 2 years ago | |
| Jupyter Notebook | Python | |
| MIT License | MIT License |
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DeepLearning
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Help with my PyTorch implementation of PPO
I implemented PPO using PyTorch here. As is suggested, I was trying it on very simple environment (CartPole-v1).
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.
What are some alternatives?
AI-For-Beginners - 12 Weeks, 24 Lessons, AI for All!
TensorLayer - Deep Learning and Reinforcement Learning Library for Scientists and Engineers
cs231n - Note and Assignments for CS231n: Convolutional Neural Networks for Visual Recognition
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
conformal_classification - Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
analisis-numerico-computo-cientifico - Análisis numérico y cómputo científico
deep-q-learning - Minimal Deep Q Learning (DQN & DDQN) implementations in Keras
Deep-Learning-Experiments - Videos, notes and experiments to understand deep learning
TensorFlow2.0-for-Deep-Reinforcement-Learning - TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
weightless_NN_decompression - Proof of concept for neural network decompression without storing any weights
acer - PyTorch implementation of both discrete and continuous ACER