Deep Q-Learning agent learns how to navigate a world full of bananas. Part of the coursework for Udacity's Deep RL Nanodegree.
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Updated
Sep 7, 2021 - Jupyter Notebook
Deep Q-Learning agent learns how to navigate a world full of bananas. Part of the coursework for Udacity's Deep RL Nanodegree.
Reinforcement learning project. The objective is to learn an asymmetric distance function over states that will allow goal-pursuing.
An intelligent PACMAN agent built using Reinforcement learning.
심층강화학습기반 장애물과 신호등을 고려한 다차선 자율주행 연구
A Reinforcement Learning library for solving custom environments
A RL agent that learns to play doom's deadly corridor based on DDQN and PER.
Slide presentation reviewing advances in reinforcement learning
Project 2 of Udacity's Deep Reinforcement Learning NanoDegree
Example Rainbow DQN implementation with ReLAx
Reinforcement Learning Agents for Analog Circuit Sizing in Haskell.
Tests SOTA algorithms using pendulum as baseline environment
reinforcement learning framework with pytorch
Deep Reinforcement Learning on Lunar Lander gym environment
An implementation of Deep Q-Learning Network for solving a Unity environment that can navigate and collect bananas in a large, square world.
Reinforcement learning of point to point reaching
Prioritized DDQN example with ReLAx
Third homework for the Reinforcement Learning course
Computational neuroscience project on priority access to memory through reinforcement learning.
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