Training a vision-based agent with the Deep Q Learning Network (DQN) in Atari's Breakout environment, implementation in Tensorflow.
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Updated
Dec 12, 2018 - Python
Training a vision-based agent with the Deep Q Learning Network (DQN) in Atari's Breakout environment, implementation in Tensorflow.
Difficult and annoying Tetris implemented by Reinforcement-Learning
Reinforcement learning framework for implementing custom models on custom environments using state of the art RL algorithms
Tensorflow based DQN and PyTorch based DDQN Agent for 'MountainCar-v0' openai-gym environment.
This project has purpose training an DQN Agent to recognize malware traffic.
The goal of this project is to apply what I learned of Reinforcement Learning from the course "Reinforcement Learning Specialization" of University of Alberta & Alberta Machine Intelligence Institute on Coursera.
Implementation of Deep Recurrent Q-Networks for Partially Observable environment setting in Tensorflow
Self Driving Car using Deep Q-Learning Networks
Agent will compare the usage of Neural Network with heuristic vs Deep-Q-Network (DQN) learning to increasingly improve itself on playing a Snake game.
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