N++2 is a multi-threaded neural network simulator with network sharding and BLAS support. It was developed from 2007 to 2010 in order to realize training and realtime inference of deep autoencoder networks. With its help I created the first visual DeepRL control policies.

This project contains several Deep Reinforcement Learning method and some experiments basd on OpenAi gym.

PPO with Hindsight Experience Replay (HER)

DeepRL para Forex Trading

이 저장소는 강화학습의 학습 방법 중 하나인 정책 경사(policy gradient)에 대한 방법론을 정리합니다.

Soccer3D Deep RL Framework

Deep Reinforcement Learning Lab, a platform designed to make DRL technology and fun for everyone focusing on 交流方式, 3、一流研究机构有哪些?, 4、业界大佬有哪些？, 联系方式, 5、怎么学习深度强化学习.

Sandbox for Deep Reinforcement Learning Algorithms

OpenAI CartPole-v0 DeepRL-based solutions (DQN, DuelingDQN, D3QN)

OpenAI MountainCar-v0 DeepRL-based solutions (DQN, DuelingDQN, D3QN)

OpenAI LunarLander-v2 DeepRL-based solutions (DQN, DuelingDQN, D3QN)

Atari OpenAI Pong-v4 DeepRL-based solutions (DQN, DuelingDQN, D3QN)

TensorFlow2 implementation of Self-Imitation Learning (SIL) with Synchronous Advantage Actor-Critic (A2C).

2022 Spring Semester, Personal Project Research

You can see a reference for Books, Articles, Courses and Educational Materials in this field. Implementation of Reinforcement Learning Algorithms and Environments. Python, OpenAI Gym, Tensorflow.

My Solution to Berkeley CS285 Deep Reinforcement Learning Fall22

Implementation of Continuous Control RL Algorithms

This repository contains the implementation of a wide variety of Reinforcement Learning Projects in different applications of Bandit Algorithms, MDPs, Distributed RL and Deep RL. These projects include university projects and projects implemented due to interest in Reinforcement Learning.

My own implementation of deep reinforcement learning algorithms.

This project is an experiment that applies in real-time the style of famous paintings to popular fighting retro games, which are provided as Reinforcement Learning environments by DIAMBRA. It is based on the paper "Perceptual Losses for Real-Time Style Transfer and Super-Resolution".