chainerrl
deep-q-learning
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| chainerrl | deep-q-learning | |
|---|---|---|
| 3 | 1 | |
| 1,141 | 1,209 | |
| 0.0% | - | |
| 0.0 | 0.0 | |
| over 2 years ago | over 3 years ago | |
| Python | Python | |
| MIT License | 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.
deep-q-learning
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Deep Q Network knapsack problem
So go online on GitHub and find a DQN implementation that has options for using a feedforward net as input (instead of conv net as your input isn’t pixel based). Any remotely modular piece of code will take in state space size and action space as parameters to their NN. This is essentially setting input layer to be equal to state space (so 4) and output layer to be action space (201). (https://github.com/keon/deep-q-learning) this repo seems helpful i a cursory glance
What are some alternatives?
TensorLayer - Deep Learning and Reinforcement Learning Library for Scientists and Engineers
Tetris-deep-Q-learning-pytorch - Deep Q-learning for playing tetris game
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
deep-RL-trading - playing idealized trading games with deep reinforcement learning
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
DeepLearning - Contains all my works, references for deep learning
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.
TensorFlow2.0-for-Deep-Reinforcement-Learning - TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
acer - PyTorch implementation of both discrete and continuous ACER
fundamentalRL - educational codebase demonstrating some of the most common RL algorithms