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deep-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-algorithmsdeep-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 dqnPyTorch-Tutorial - Build your neural network easy and fast
In these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for liufuyang's notebook files which is a great contribution to this tutorial.
neural-network pytorch-tutorial batch-normalization cnn rnn autoencoder pytorch regression classification batch tutorial dropout dqn reinforcement-learning gan generative-adversarial-network machine-learningReinforcement-learning-with-tensorflow - Simple Reinforcement learning tutorials
In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years. If you speak Chinese, visit 莫烦 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-learningTensorflow-Tutorial - Tensorflow tutorial from basic to hard
In these tutorials, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. All methods mentioned below have their video and text tutorial in Chinese. Visit 莫烦 Python for more.
tensorflow tensorflow-tutorials gan generative-adversarial-network rnn cnn classification regression autoencoder deep-q-network dqn machine-learning tutorial dropout neural-networktensorlayer - Deep Learning and Reinforcement Learning Library for Developers and Scientists
TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides a large collection of customizable neural layers / functions that are key to build real-world AI applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. Simplicity : TensorLayer lifts the low-level dataflow interface of TensorFlow to high-level layers / models. It is very easy to learn through the rich example codes contributed by a wide community.
tensorlayer deep-learning tensorflow machine-learning data-science neural-network reinforcement-learning artificial-intelligence gan a3c tensorflow-tutorials dqn object-detection chatbot tensorflow-tutorial imagenet googleTensorFlow-Tutorials - 텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다
텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다. 텐서플로우 공식 사이트에서 제공하는 안내서의 대부분의 내용을 다루고 있으며, 공식 사이트에서 제공하는 소스 코드보다는 훨씬 간략하게 작성하였으므로 쉽게 개념을 익힐 수 있을 것 입니다. 또한, 모든 주석은 한글로(!) 되어 있습니다.
neural-network tensorflow mnist autoencoder rnn deep-learning tutorial chatbot seq2seq dqn word2vec cnn gan inceptiondeep-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 ddqnchainerrl - ChainerRL is a deep reinforcement learning library built on top of Chainer.
ChainerRL 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-criticSLM-Lab - Modular Deep Reinforcement Learning framework in PyTorch.
Modular 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-rlrl4j - Deep Reinforcement Learning for the JVM (Deep-Q, A3C)
Deep Reinforcement Learning for the JVM
reinforcement-learning deeplearning4j doom cartpole a3c dqn gym-java-client artificial-intelligencechainer-notebooks - Jupyter notebooks for Chainer hands-on
Jupyter notebooks for Chainer hands-on
chainer deep-learning jupyter-notebook dqn word2vec rnn language-model cnn convolutional-neural-networksReinforcement-Learning - 🤖 Implements of Reinforcement Learning algorithms.
This repo is implements of Reinforcement Learning Algorithms, implementing as learning, some of them are even another version of some tutorial. Any contributions are welcomed. Deep Deterministic Policy Gradient (DDPG) Implement of DDPG.
reinforcement-learning reinforcement-learning-algorithms papers dqn ddpg deep-reinforcement-learning deep-learning-algorithmsctc-executioner - Master Thesis: Limit order placement with Reinforcement Learning
CTC-Executioner is a tool that provides an on-demand execution/placement strategy for limit orders on crypto currency markets using Reinforcement Learning techniques. The underlying framework provides functionalities which allow to analyse order book data and derive features thereof. Those findings can then be used in order to dynamically update the decision making process of the execution strategy. The methods being used are based on a research project (master thesis) currently proceeding at TU Delft.
openai-gym openai-gym-environment openai-gym-agents execution-strategy reinforcement-learning order-placement limit-order-book match-engine dqn q-learning market-makerdeeprl-baselines - Deep reinforcement learning baselines base on OpenAI
Our code is based on OpenAI Baselines, which is a set of high-quality implementations of reinforcement learning algorithms. Our code is aimed to provide more algorithms which is not included by OpenAI baselines, such as C51 and rainbow, as well as improvements. These algorithms will make it easier for the research community to replicate, refine, and identify new ideas, and will create good baselines to build research on top of. Our DQN implementation and its variants are roughly on par with the scores in published papers. We expect they will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones.
deep-reinforcement-learning rainbow dqnrl_implementations
Some reinforcement learning algorithms I'm (re)-implementing, all in one place. Also is a dumping ground for other ML work.
reptile reinforcement-learning policy-gradient dqn deep-learning deep-q-network deep-q-learning deep-reinforcement-learning lstm ddpg machine-learning meta-learning hierarchical-reinforcement-learning a2c actor-critictorchrl - Highly Modular and Scalable Reinforcement Learning
TorchRL provides highly modular and extensible approach to experimenting with Reinforcement Learning. It allows for a registry based approach to running experiments, allows easy checkpointing, and updating hyper parameter sets. All this is accessible via a programmatic interface as well as a friendly CLI. Install from source for the latest changes that have not been published to PyPI.
machine-learning reinforcement-learning reinforcement-learning-algorithms pytorch deep-learning deep-reinforcement-learning python3 dqn policy-gradientAction_Detection_DQN - Lua
The paper can be found here. Self-Adaptive Proposal (SAP) is a DQN based model for temporal action localization in untrimmed long videos. The temporal action detection process for SAP is naturally one of observation and refinement: observe the current window and refine the span of attended window to cover true action regions. SAP can learn to find actions through continuously adjusting the temporal bounds in a self-adaptive way. Experiment results on THUMOS’14 validate the effectiveness of SAP, which can achieve competitive performance with current action detection algorithms via much fewer proposals.
sap dqn action-detection temporal-proposalHands-On-Intelligent-Agents-with-OpenAI-Gym - Code for Hands On Intelligent Agents with OpenAI Gym book to get started and learn to build deep reinforcement learning agents using PyTorch
HOIAWOG!: Your guide to developing AI agents using deep reinforcement learning. Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator.
intelligent-agents deep-reinforcement-learning openai-gym carla-simulator dqn pytorch learning-agents pytorch-a3c pytorch-carla advantage-actor-criticking-pong - Deep Reinforcement Learning Pong Agent, King Pong, he's the best
In this repository, you have an agent that plays the game of pong. Make no mistake though, this is not a normal player. King (the agent) has learned to play the game of pong all by himself, by looking at the screen just like you would. Now, as you can imagine, there are a lot of cutting edge technologies being mixed into this project. First, we have Computer Vision to be able to receive the percepts from the screen. Next, we have Reinforcement Learning which is part of Machine Learning, but it is not classification, nor regression, or clustering. Reinforcement Learning is inspired by the study of animal behavior. In specific, how animals react to pain, reward signals through time. King wants to win, that's why he learns to do what he does.
deep-reinforcement-learning deep-learning deep-q-network dqn q-learning agent machine-learning king-pong percept reinforcement-learning reinforcement-learning-algorithms
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