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NOTICE: Please use the next version, SLM-Lab. An experimentation framework for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.

https://github.com/kengz/openai_lab#readmehttps://github.com/kengz/openai_lab

Dependencies:

config : ^1.25.1

grunt : ^1.0.1

grunt-concurrent : ^2.3.1

grunt-contrib-watch : ^1.0.0

grunt-shell : ^2.1.0

grunt-sync : ^0.6.2

load-grunt-tasks : ^3.5.2

lodash : ^4.17.4

resolve-dir : ^1.0.0

snyk : ^1.41.1

Tags | keras tensorflow openai experiment policy-gradient actor-critic ddpg deep-reinforcement-learning reinforcement-learning gym lab reinforcement learning |

Implementation | Python |

License | MIT |

Platform | Windows Linux |

Reinforcement Learning with Python will help you to master basic reinforcement learning algorithms to the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI and Tensorflow. You will then explore various RL algorithms and concepts such as the Markov Decision Processes, Monte-Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep learning, covering various deep learning algorithms. You will then explore deep reinforcement learning in depth, which is a combination of deep learning and reinforcement learning. You will master various deep reinforcement learning algorithms such as DQN, Double DQN. Dueling DQN, DRQN, A3C, DDPG, TRPO, and PPO. You will also learn about recent advancements in reinforcement learning such as imagination augmented agents, learn from human preference, DQfD, HER and many more.

reinforcement-learning deep-reinforcement-learning sarsa q-learning policy-gradients deep-q-network deep-learning-algorithms asynchronous-advantage-actor-critic deep-deterministic-policy-gradient deep-recurrent-q-network double-dqn dueling-dqn hindsight-experience-replay drqn trpo ppoModular Deep Reinforcement Learning framework in PyTorch. A multitask agent solving both OpenAI Cartpole-v0 and Unity Ball2D.

reinforcement-learning pytorch openai-gym framework research dqn artificial-intelligence policy-gradient actor-critic ppo a3c deep-rlkeras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Just like Keras, it works with either Theano or TensorFlow, which means that you can train your algorithm efficiently either on CPU or GPU. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and thus Theano or TensorFlow and was built with OpenAI Gym in mind.

keras tensorflow theano reinforcement-learning neural-networks machine-learningIn these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years. If you speak Chinese, visit θ«η¦ Python or my Youtube channel for more.

reinforcement-learning tutorial q-learning sarsa sarsa-lambda deep-q-network a3c ddpg policy-gradient dqn double-dqn prioritized-replay dueling-dqn deep-deterministic-policy-gradient asynchronous-advantage-actor-critic actor-critic tensorflow-tutorials proximal-policy-optimization ppo machine-learningThis repository contains material related to Udacity's Deep Reinforcement Learning Nanodegree program. The tutorials lead you through implementing various algorithms in reinforcement learning. All of the code is in PyTorch (v0.4) and Python 3.

deep-reinforcement-learning reinforcement-learning reinforcement-learning-algorithms neural-networks pytorch pytorch-rl ddpg dqn ppo dynamic-programming cross-entropy hill-climbing ml-agents openai-gym-solutions openai-gym rl-algorithmsIt is still a problem to implement Batch Normalization on the critic network. However the actor network works well with Batch Normalization. Some Mujoco environments are still unsolved on OpenAI Gym.

This is a Tensorflow + Keras implementation of asyncronous 1-step Q learning as described in "Asynchronous Methods for Deep Reinforcement Learning". Since we're using multiple actor-learner threads to stabilize learning in place of experience replay (which is super memory intensive), this runs comfortably on a macbook w/ 4g of ram.

Coach is a python reinforcement learning research framework containing implementation of many state-of-the-art algorithms. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms, and allows simple integration of new environments to solve. Basic RL components (algorithms, environments, neural network architectures, exploration policies, ...) are well decoupled, so that extending and reusing existing components is fairly painless.

coach openai-gym reinforcement-learning tensorflow rl carla imitation-learning mujoco roboschool deep-learning hierarchical-reinforcement-learning starcraft starcraft2 starcraft2-aiIn this tutorial, we'll be creating artificially intelligent agents that learn from interacting with their environment, gathering experience, and a system of rewards with deep reinforcement learning (deep RL). Using end-to-end neural networks that translate raw pixels into actions, RL-trained agents are capable of exhibiting intuitive behaviors and performing complex tasks. Ultimately, our aim will be to train reinforcement learning agents from virtual robotic simulation in 3D and transfer the agent to a real-world robot. Reinforcement learners choose the best action for the agent to perform based on environmental state (like camera inputs) and rewards that provide feedback to the agent about it's performance. Reinforcement learning can learn to behave optimally in it's environment given a policy, or task - like obtaining the reward.

Attempting to replicate "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" by Jiang et. al. 2017 [1]. This paper trains an agent to choose a good portfolio of cryptocurrencies. It's reported that it can give 4-fold returns in 50 days and the paper seems to do all the right things so I wanted to see if I could acheive the same results.

portfolio-management deep-reinforcement-learning deeprl cryptocurrency openai-gym-environments openai-gymDeep Reinforcement Learning Course is a free series of blog posts and videos π about Deep Reinforcement Learning, where we'll learn the main algorithms, and how to implement them with Tensorflow. πThe articles explain the concept from the big picture to the mathematical details behind it.

deep-reinforcement-learning qlearning deep-learning tensorflow-tutorials tensorflow ppo a2c actor-critic deep-q-network deep-q-learningThis project provides optimized infrastructure for reinforcement learning. It extends the OpenAI gym interface to multiple parallel environments and allows agents to be implemented in TensorFlow and perform batched computation. As a starting point, we provide BatchPPO, an optimized implementation of Proximal Policy Optimization. The algorithm to use is defined in the configuration and pendulum started here uses the included PPO implementation. Check out more pre-defined configurations in agents/scripts/configs.py.

reinforcement-learning tensorflow multi-processing artificial-intelligence vectorized-computation controlVanilla A3C code is based on the open source implementation of universe-starter-agent.

deep-reinforcement-learning curiosity exploration deep-learning rl deep-neural-networks mario doom self-supervised tensorflow openai-gymNOTE: THE CODE IS UNDER DEVELOPMENT, PLEASE ALWAYS PULL THE LATEST VERSION FROM HERE. In recent years, sequence-to-sequence (seq2seq) models are used in a variety of tasks from machine translation, headline generation, text summarization, speech to text, to image caption generation. The underlying framework of all these models are usually a deep neural network which contains an encoder and decoder. The encoder processes the input data and a decoder receives the output of the encoder and generates the final output. Although simply using an encoder/decoder model would, most of the time, produce better result than traditional methods on the above-mentioned tasks, researchers proposed additional improvements over these sequence to sequence models, like using an attention-based model over the input, pointer-generation models, and self-attention models. However, all these seq2seq models suffer from two common problems: 1) exposure bias and 2) inconsistency between train/test measurement. Recently a completely fresh point of view emerged in solving these two problems in seq2seq models by using methods in Reinforcement Learning (RL). In these new researches, we try to look at the seq2seq problems from the RL point of view and we try to come up with a formulation that could combine the power of RL methods in decision-making and sequence to sequence models in remembering long memories. In this paper, we will summarize some of the most recent frameworks that combines concepts from RL world to the deep neural network area and explain how these two areas could benefit from each other in solving complex seq2seq tasks. In the end, we will provide insights on some of the problems of the current existing models and how we can improve them with better RL models. We also provide the source code for implementing most of the models that will be discussed in this paper on the complex task of abstractive text summarization.

reinforcement-learning actor-critic policy-gradient abstractive-text-summarization pointer-generator nlpGym StarCraft is an environment bundle for OpenAI Gym. It is based on Facebook's TorchCraft, which is a bridge between Torch and StarCraft for AI research.Install OpenAI Gym and its dependencies.

openai-gym gym-starcraft starcraft reinforcement-learning artificial-intelligence deep-learningThis repository aims to provide an introduction series to reinforcement learning (RL) by delivering a walkthough on how to code different RL techniques. A quick background review of RL is available here.

reinforcement-learning openai-gym tutorial deeplearning neural-networks deep-learning artificial-intelligence q-learning aiThis repository contains the code and pdf of a series of blog post called "dissecting reinforcement learning" which I published on my blog mpatacchiola.io/blog. Moreover there are links to resources that can be useful for a reinforcement learning practitioner. If you have some good references which may be of interest please send me a pull request and I will integrate them in the README. The source code is contained in src with the name of the subfolders following the post number. In pdf there are the A3 documents of each post for offline reading. In images there are the raw svg file containing the images used in each post.

reinforcement-learning deep-reinforcement-learning markov-chain temporal-differencing-learning sarsa q-learning actor-critic multi-armed-bandit inverted-pendulum mountain-car drone-landing dissecting-reinforcement-learning genetic-algorithmFirst, you will need to install git, if you don't have it already. If you want to go through chapter 16 on Reinforcement Learning, you will need to install OpenAI gym and its dependencies for Atari simulations.

tensorflow scikit-learn machine-learning deep-learning neural-network ml distributed jupyter-notebookChainerRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. ChainerRL is tested with Python 2.7+ and 3.5.1+. For other requirements, see requirements.txt.

chainer reinforcement-learning deep-learning machine-learning dqn actor-criticOpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. This is the gym open-source library, which gives you access to a standardized set of environments. gym makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. You can use it from Python code, and soon from other languages.

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