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ml-agents - Unity Machine Learning Agents
Unity Machine Learning Agents (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on TensorFlow) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. ML-Agents is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity’s rich environments and then made accessible to the wider research and game developer communities. For more information, in addition to installation and usage instructions, see our documentation home. If you have used a version of ML-Agents prior to v0.3, we strongly recommend our guide on migrating to v0.3.
reinforcement-learning unity3d deep-learning unity deep-reinforcement-learning neural-networksdeep-reinforcement-learning - Repo for the Deep Reinforcement Learning Nanodegree program
This 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-algorithmsPractical_RL - A course in reinforcement learning in the wild
A course on reinforcement learning in the wild. Taught on-campus at HSE and YSDA and maintained to be friendly to online students (both english and russian). The syllabus is approximate: the lectures may occur in a slightly different order and some topics may end up taking two weeks.
reinforcement-learning course-materials deep-learning deep-reinforcement-learning git-course mooc theano lasagne tensorflow pytorch pytorch-tutorials kerasdeep-rl-tensorflow - TensorFlow implementation of Deep Reinforcement Learning papers
Result of Corridor-v5 in [4] for DQN (purple), DDQN (red), Dueling DQN (green), Dueling DDQN (blue).
tensorflow deep-reinforcement-learning dqnML-From-Scratch - Machine Learning From Scratch
Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible but rather to present the inner workings of them in a transparent and accessible way.
machine-learning deep-learning deep-reinforcement-learning machine-learning-from-scratch data-science data-mining genetic-algorithmtensorforce - TensorForce: A TensorFlow library for applied reinforcement learning
TensorForce is an open source reinforcement learning library focused on providing clear APIs, readability and modularisation to deploy reinforcement learning solutions both in research and practice. TensorForce is built on top of TensorFlow and compatible with Python 2.7 and >3.5 and supports multiple state inputs and multi-dimensional actions to be compatible with any type of simulation or application environment. TensorForce also aims to move all reinforcement learning logic into the TensorFlow graph, including control flow. This both reduces dependencies on the host language (Python), thus enabling portable computation graphs that can be used in other languages and contexts, and improves performance.
reinforcement-learning tensorflow deep-reinforcement-learning deep-q-networkDeepLearningFlappyBird - Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).
This project follows the description of the Deep Q Learning algorithm described in Playing Atari with Deep Reinforcement Learning [2] and shows that this learning algorithm can be further generalized to the notorious Flappy Bird. It is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards.
deep-learning deep-reinforcement-learning gamecarla - Open-source simulator for autonomous driving research.
CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. If you want to benchmark your model in the same conditions as in our CoRL’17 paper, check out Benchmarking.
simulator autonomous-vehicles autonomous-driving research ai artificial-intelligence computer-vision deep-learning deep-reinforcement-learning imitation-learning self-driving-car ue4 unreal-engine-4 cross-platformnoreward-rl - [ICML 2017] TensorFlow code for Curiosity-driven Exploration for Deep Reinforcement Learning
Vanilla 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-gymtensorflow-tutorial - TensorFlow and Deep Learning Tutorials
Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.
recurrent-neural-networks convolutional-neural-networks deep-learning-tutorial tensorflow tensorlayer keras deep-reinforcement-learning tensorflow-tutorials deep-learning machine-learning notebook autoencoder multi-layer-perceptron reinforcement-learning tflearn neural-networks neural-network neural-machine-translation nlp cnnAirSim - Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research
AirSim is a simulator for drones (and soon other vehicles) built on Unreal Engine. It is open-source, cross platform and supports hardware-in-loop with popular flight controllers such as PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped in to any Unreal environment you want.
drones ai self-driving-car autonomous-vehicles autonomous-quadcoptor research computer-vision artificial-intelligence deeplearning deep-reinforcement-learning control-systems pixhawk cross-platform platform-independent airsim unreal-engine simulatordeep-q-learning - Minimal Deep Q Learning (DQN & DDQN) implementations in Keras
I made minor tweaks to this repository such as load and save functions for convenience. I also made the memory a deque instead of just a list. This is in order to limit the maximum number of elements in the memory.
deep-reinforcement-learning deep-q-network dqn reinforcement-learning deep-learning ddqnTop-Deep-Learning - Top 200 deep learning Github repositories sorted by the number of stars.
Trending deep learning Github repositories can be found here. Hint: This will be updated regularly.
deep-learning deep-neural-networks deep-reinforcement-learning convolutional-neural-networks recurrent-neural-networks stargazers-count artificial-neural-networks artificial-intelligence machine-learning top-repositoriesDeep_reinforcement_learning_Course - Implementations from the free course Deep Reinforcement Learning with Tensorflow
Deep Reinforcement Learning Course is a free series of blog posts and videos 🆕 about Deep Reinforcement Learning, where we'll learn the main algorithms, and how to implement them with Tensorflow. 📜The 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-learningRainbow - Rainbow: Combining Improvements in Deep Reinforcement Learning
Rainbow: Combining Improvements in Deep Reinforcement Learning [1]. Results and pretrained models can be found in the releases.
deep-learning deep-reinforcement-learningdetection-2016-nipsws - Hierarchical Object Detection with Deep Reinforcement Learning
We present a method for performing hierarchical object detection in images guided by a deep reinforcement learning agent. The key idea is to focus on those parts of the image that contain richer information and zoom on them. We train an intelligent agent that, given an image window, is capable of deciding where to focus the attention among five different predefined region candidates (smaller windows). This procedure is iterated providing a hierarchical image analysis. We compare two different candidate proposal strategies to guide the object search: with and without overlap. Moreover, our work compares two different strategies to extract features from a convolutional neural network for each region proposal: a first one that computes new feature maps for each region proposal, and a second one that computes the feature maps for the whole image to later generate crops for each region proposal.
deep-reinforcement-learning deep-learning deep-neural-networksdissecting-reinforcement-learning - Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog
This 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-algorithmGibsonEnv - Gibson Environments: Real-World Perception for Embodied Agents
You shouldn't play video games all day, so shouldn't your AI! We built a virtual environment simulator, Gibson, that offers real-world experience for learning perception. I. being from the real-world and reflecting its semantic complexity through virtualizing real spaces, II. having a baked-in mechanism for transferring to real-world (Goggles function), and III. embodiment of the agent and making it subject to constraints of space and physics via integrating a physics engine (Bulletphysics).
computer-vision robotics simulator sim2real deep-learning deep-reinforcement-learning research ros reinforcement-learning cvpr2018awesome-deep-rl - This project is for learning and researching on Deep RL
This project is built for people who are learning and researching on latest deep reinforcement learning methods. Recommendations and suggestions are welcome.
deep-reinforcement-learning reinforcement-learning game reward artificial-general-intelligence exploration-exploitation hierarchical-reinforcement-learning distributional multiagent-reinforcement-learning planning theoretical-computer-science inverse-rl icml aamas ijcai aaai aistats uai agirl-portfolio-management - Attempting to replicate "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" https://arxiv
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-gym
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