Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow
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Updated
Oct 7, 2020 - Jupyter Notebook
Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow
Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch
Repository for codes of 'Deep Reinforcement Learning'
Deep recurrent Q Learning using Tensorflow, openai/gym and openai/retro
DQN-Atari-Agents: Modularized & Parallel PyTorch implementation of several DQN Agents, i.a. DDQN, Dueling DQN, Noisy DQN, C51, Rainbow, and DRQN
This is the code implementation of the paper "Financial Trading as a Game: A Deep Reinforcement Learning Approach".
Atari-DRQN (keras ver.)
Collision Avoidance with Reinforcement Learning
To keep track and showcase
Pathfinding Using Reinforcement Learning
Multi-Agent Deep Recurrent Q-Learning with Bayesian epsilon-greedy on AirSim simulator
This is a reconstruction of previous repository(rl-algorithms).
Implementation of the DQN and DRQN algorithms in Keras and tensorflow
A multi agent reinforcement learning environment where two agents controlled by DRQNs play a custom version of the pursuit-evasion game.
Deep Recurrent Q-Network with different exploration strategies for self-driving cars (using AirSim)
Alogtrader bot using RL
hdrqn
Tensorflow implementation of Reinforcement Learning methods for Atari 2600.
Implementation of DQN, DDQN and DRQNs for Atari Games in Tensorflow. [Work in Progress]
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