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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-networkIn 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-learningIn 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 θ«η¦ Python for more.

tensorflow tensorflow-tutorials gan generative-adversarial-network rnn cnn classification regression autoencoder deep-q-network dqn machine-learning tutorial dropout neural-networkI 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 ddqnDeep 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-learningReinforcement 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 ppoDeep Q-Learning Network in pytorch

pytorch deep-q-network deep-reinforcement-learningA directed acyclic computational graph builder, built from scratch on numpy and C, with auto-differentiation supported. This was not just another deep learning library, its clean code base was supposed to be read. Great for any one who want to learn about Backprop design in deep learning libraries.

machine-learning dropout lstm mnist lenet neural-turing-machines question-answering computational-graphs auto-differentiation convolutional-neural-networks convolutional-networks recurrent-neural-networks lstm-model deep-learning deep-q-network reinforcement-learning cartpoleSome 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-criticThe intent of these IPython Notebooks are mostly to help me practice and understand the papers I read; thus, I will opt for readability over efficiency in some cases. First the implementation will be uploaded, followed by markup to explain each portion of code. I'll be assigning credit for any code which is borrowed in the Acknowledgements section of this README.

python3 pytorch reinforcement-learning deep-reinforcement-learning deep-q-network double-dqn multi-step-learning dueling-dqn noisy-networks prioritized-experience-replay deeprl-tutorials categorical-dqn rainbow quantile-regression deep-recurrent-q-network actor-critic advantage-actor-critic a2c gae ppo2048 is a single-player sliding block puzzle game designed by Italian web developer Gabriele Cirulli. The game's objective is to slide numbered tiles on a grid to combine them to create a tile with the number 2048; however, you can keep playing the game, creating tiles with larger numbers. 2048 is played on a gray 4×4 grid, with numbered tiles that slide smoothly when a player moves them using the four arrow keys.Every turn, a new tile will randomly appear in an empty spot on the board with a value of either 2 or 4. Tiles slide as far as possible in the chosen direction until they are stopped by either another tile or the edge of the grid. If two tiles of the same number collide while moving, they will merge into a tile with the total value of the two tiles that collided. The resulting tile cannot merge with another tile again in the same move. Higher-scoring tiles emit a soft glow.

reinforcement-learning deep-reinforcement-learning q-learning deep-q-learning deep-q-network 2048-game neural-network convolutional-neural-networksIn 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-algorithmsThis is pytorch implementation of distributed deep reinforcement learning. In our system, there are two processes, Actor and Learner. In Learner process, thread of the replay memory runs at the same time, and these processes communicate using Redis.

distributed-systems reinforcement-learning openai-gym pytorch amazon-web-services deep-q-network r2d2 double-dqn dueling-dqn prioritized-experience-replay ape-x
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