Displaying 1 to 12 from 12 results

tensorforce - TensorForce: A TensorFlow library for applied reinforcement learning

  •    Python

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.

Tensorflow-Tutorial - Tensorflow tutorial from basic to hard

  •    Python

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 θŽ«ηƒ¦ Python for more.

deep-q-learning - Minimal Deep Q Learning (DQN & DDQN) implementations in Keras

  •    Python

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_Course - Implementations from the free course Deep Reinforcement Learning with Tensorflow

  •    Jupyter

Deep Reinforcement Learning Course is a free series of blog posts and videos πŸ†• about Deep Reinforcement Learning, where we'll learn the main algorithms, and how to implement them with Tensorflow. πŸ“œThe articles explain the concept from the big picture to the mathematical details behind it.

Hands-On-Reinforcement-Learning-With-Python - Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

  •    Jupyter

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.

essence - AutoDiff DAG constructor, built on numpy and Cython

  •    Python

A 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.


DeepRL-Tutorials - Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

  •    Jupyter

The 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.

2048-deep-reinforcement-learning - Trained A Convolutional Neural Network To Play 2048 using Deep-Reinforcement Learning

  •    Jupyter

2048 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.

king-pong - Deep Reinforcement Learning Pong Agent, King Pong, he's the best

  •    Python

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.