Displaying 1 to 9 from 9 results

deep-reinforcement-learning - Repo for the Deep Reinforcement Learning Nanodegree program

  •    Jupyter

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-rl-tensorflow - TensorFlow implementation of Deep Reinforcement Learning papers

  •    Python

Result of Corridor-v5 in [4] for DQN (purple), DDQN (red), Dueling DQN (green), Dueling DDQN (blue).

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.

Reinforcement-Learning - 🤖 Implements of Reinforcement Learning algorithms.

  •    Python

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.




deeprl-baselines - Deep reinforcement learning baselines base on OpenAI

  •    Python

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.

torchrl - Highly Modular and Scalable Reinforcement Learning

  •    Python

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.

Hands-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

  •    Python

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.


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.