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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 cnnpwnagotchi - (⌐■_■) - Deep Reinforcement Learning instrumenting bettercap for WiFi pwning.
Pwnagotchi is an A2C-based "AI" leveraging bettercap that learns from its surrounding WiFi environment to maximize the crackable WPA key material it captures (either passively, or by performing authentication and association attacks). This material is collected as PCAP files containing any form of handshake supported by hashcat, including PMKIDs, full and half WPA handshakes. Instead of merely playing Super Mario or Atari games like most reinforcement learning-based "AI" (yawn), Pwnagotchi tunes its parameters over time to get better at pwning WiFi things to in the environments you expose it to.
ai deep-learning deep-reinforcement-learning wpa-psk bettercap deep-neural-network handshakesAwesome-System-for-Machine-Learning - A curated list of research in machine learning system
A curated list of research in machine learning system. Link to the code if available is also present. Now we have a team to maintain this project. You are very welcome to pull request by using our template.
infrastructure distributed-systems machine-learning deep-neural-networks system deep-learning optimization paper deep-reinforcement-learning inference automl computer-system edge-computing model-database resouce-managementtensorforce - Tensorforce: a TensorFlow library for applied reinforcement learning
Tensorforce is an open-source deep reinforcement learning framework, with an emphasis on modularized flexible library design and straightforward usability for applications in research and practice. Tensorforce is built on top of Google's TensorFlow framework and requires Python 3. Note on installation on M1 Macs: At the moment Tensorflow, which is a core dependency of Tensorforce, cannot be installed on M1 Macs directly. Follow the "M1 Macs" section in the documentation for a workaround.
control reinforcement-learning tensorflow deep-reinforcement-learning tensorflow-library system-control tensorforceDeep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020 - Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy
This ensemble strategy is reimplemented in a Jupiter Notebook at FinRL. Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose a deep ensemble reinforcement learning scheme that automatically learns a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using the three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market conditions. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand approach for processing very large data. We test our algorithms on the 30 Dow Jones stocks which have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble scheme is shown to outperform the three individual algorithms and the two baselines in terms of the risk-adjusted return measured by the Sharpe ratio.
deep-reinforcement-learning openai-gym sharpe-ratio ddpg stock-trading ppo a2c-algorithm ensemble-strategy stock-trading-strategy automated-stock-tradingElegantRL - Lightweight, efficient and stable implementations of deep reinforcement learning algorithms using PyTorch
ElegantRL is featured with lightweight, efficient and stable, for researchers and practitioners. Lightweight: The core codes <1,000 lines (check elegantrl/tutorial), using PyTorch (train), OpenAI Gym (env), NumPy, Matplotlib (plot).
lightweight reinforcement-learning gae deep-reinforcement-learning efficient pytorch stable dqn ddpg sac ppo td3 soft-actor-critic model-free-rl drl-pytorch bipedalwalkerhardcoreFinRL - A Deep Reinforcement Learning Library for Automated Trading in Quantitative Finance
FinRL is an open source library that provides practitioners a unified framework for pipeline strategy development. In reinforcement learning (or Deep RL), an agent learns by continuously interacting with an environment, in a trial-and-error manner, making sequential decisions under uncertainty and achieving a balance between exploration and exploitation. The open source community AI4Finance (to efficiently automate trading) provides educational resources about deep reinforcement learning (DRL) in quantitative finance. To contribute? Please check the end of this page.
finance deep-reinforcement-learning openai-gym fintech stock-trading multi-agent-learning stock-markets pythorch tensorflow2 drl-trading-agents drl-algorithms finrl-library drl-framework trading-taskspytorch-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)
PPO is great, but Soft Actor Critic can be better for many continuous control tasks. Please check out my new RL repository in jax. Also see the OpenAI posts: A2C/ACKTR and PPO for more information.
reinforcement-learning deep-learning deep-reinforcement-learning pytorch atari hessian second-order continuous-control actor-critic ale mujoco proximal-policy-optimization ppo advantage-actor-critic a2c acktr natural-gradients roboschool kfac kronecker-factored-approximationpytorch-a3c - PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning"
This is a PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning". This implementation is inspired by Universe Starter Agent. In contrast to the starter agent, it uses an optimizer with shared statistics as in the original paper.
reinforcement-learning deep-learning deep-reinforcement-learning pytorch a3c asynchronous-methods actor-critic pytorch-a3c asynchronous-advantage-actor-critic asynchDeepRL - Modularized Implementation of Deep RL Algorithms in PyTorch
If you have any question or want to report a bug, please open an issue instead of emailing me directly. Modularized implementation of popular deep RL algorithms in PyTorch. Easy switch between toy tasks and challenging games.
deep-reinforcement-learning rainbow pytorch dqn ddpg double-dqn dueling-network-architecture quantile-regression option-critic-architecture deeprl categorical-dqn ppo a2c prioritized-experience-replay option-critic td3habitat-lab - A modular high-level library to train embodied AI agents across a variety of tasks, environments, and simulators
Habitat Lab is a modular high-level library for end-to-end development in embodied AI -- defining embodied AI tasks (e.g. navigation, instruction following, question answering), configuring embodied agents (physical form, sensors, capabilities), training these agents (via imitation or reinforcement learning, or no learning at all as in classical SLAM), and benchmarking their performance on the defined tasks using standard metrics. Habitat Lab currently uses Habitat-Sim as the core simulator, but is designed with a modular abstraction for the simulator backend to maintain compatibility over multiple simulators. For documentation refer here.
simulator research reinforcement-learning ai computer-vision deep-learning robotics deep-reinforcement-learning sim2real
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