Explore projects
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Application of deep Q-Learning to play Snake.
Project for the Machine Learning course, A.Y. 2015/2016, Politecnico di Milano
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Using a Deep Q Network (a kind of Reinforcement Learning agent) to learn a policy to cross a busy intersection
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Ilya Orson Sandoval / BatchReactor
MIT LicensePart of the code experimets related to this publication: https://www.sciencedirect.com/science/article/pii/B9780128186343501545
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Duc-Tuyen Ta / LoRaWan-ML
MIT LicenseMachine Learning (ML) algorithm for the resource allocation problems in the Low Power Wide Area Network (LPWAN).
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Understanding why DeepMind's DQN is not good enough for some Atari 2600 games.
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This project aims is to learn adaptive parameter updates of nonlinear programming solvers by using reinforcement learning techniques.
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Multi-agent implementations of Reinforcement Learning algorithm Proximal Policy Optimization.
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A simple harbour simulation to test multi-agent reinforcement learning algorithms based on multi-agent-particle-envs from OpenAi
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The Simple Pokémon Environment is an AI environment complient with gym that allows for training Reinforcement Learning agents in a simplified version of Pokémon Battles.
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A Keras-RL based DQL agent that can learn to play pong.
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NETMODE / RL-driven Interactive Recommender based on SocioEmotional Behavioural Models
Apache License 2.0RL-driven Interactive Recommender based on SocioEmotional Behavioural Models
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An attempt at developing a learning agent to train an enemy mob in a mario like game
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Using reinforcement learning methods (Monte-Carlo algorithm) to create an AI in tic-tac-toe
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Simão Reis / VGC AI Framework
MIT LicenseThe VGC AI Framework aims to emulate the Esports scenario of human video game championships of Pokémon with AI agents, including the game balance aspect.
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