Implementation of RL algorithms to beat Atari 2600 games
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Updated
Sep 8, 2019 - Python
Implementation of RL algorithms to beat Atari 2600 games
This project uses deep RL to train an agent that can play Atari game named Space Invaders.
Basic code for reinforcement learning and small programs.
This repo contains lessons, notes, assignments and a final project from the reinforcement learning lesson at the ozyegin university.
Experiments on Breakout game applying Reinforcement Learning techniques
Deep Reinforcement Learning using pytorch - Bananas
Project1 Navigation
Pytorch Implementation of Double DQN Algorithm
Training PPO with DQN as a critic
DQN, Double DQN, Dueling Network
Project 1 of Udacity Deep Reinforcement Learning Nanodegree
This project investigates the intuitions/ideas behind Double DQN, and evaluate how much it can improve Q-value overestimation and agent performance. We aim to describe how the learning/update process in Double DQN ends up with better Q-value estimates and agent performance when comparing to that of DQN.
gym environnement to simulate the energetic behaviour of a real estate
Build and test DRL algorithms in different environments
Implementation of the bit-flipping experiment in the HER paper
The repository contains implementation of an AI agent to navigate and collect maximum yellow bananas, in the given environment using various deep reinforcement learning algorithms.
Space Game is the Unity 2D game for training computer controlling ships by NN.
The project implements a reinforcement learning agent that can play the Space Invaders Atari game. I compare the performance of the agent using Double Deep Q-Learning with simple Deep Q-Learning.
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