Master's Thesis - comparison of reinforcement learning frameworks
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The document outlines a master's thesis proposal focused on comparing various reinforcement learning (RL) frameworks, emphasizing their application in complex environments and recent advancements in algorithms. It includes planned activities such as theoretical study, framework installation, and real application adaptation, targeting students with specific qualifications in related fields. The project duration is 5-6 months, and interested candidates can contact the provided email or LinkedIn link.
![MASTERTHESISPROPOSALComparison of Reinforcement Learning Frameworks
MASTERTHESISPROPOSAL
Reinforcement Learning (RL) is a class of machine learning algorithms in which an agent interacts by trial-and-error in an
environment.
RL in conjunction with Deep Learning has obtained outstanding results in Atari video games, the Go board-game and a
more complex environment like StarCraft II.
Recently many open source RL frameworks has been released by software companies in order to easily train and test new
RL algorithms.
The goal of the thesis is to benchmark the most promising RL frameworks, to study the new algorithms proposed and to
evaluate their performance on research environments.
Planned Activities
1.Acquire strong theoretical basis on Deep Reinforcement
Learning;
2.Install and compare the different RL frameworks;
3.Adapt and apply the best framework to a real application.
Required Skills:
• Experience with Linux or Unix based OS;
• Proficiency in at least one programming language (Python,
Lua, Matlab, C++, Java);
• Basic knowledge of machine learning;
• Good knowledge of linear algebra.
Competencies to be acquired:
• Experience with the application of Machine Learning to
complex systems.
• Expertise on the most recent Deep Reinforcement Learning
algorithms;
• Proficient use of the most advanced development
frameworks with a software engineering approach.
add-for.com
Who we’re looking for
Students that are about to get their Master Degree in:
computer science, computer engineering, mechatronic
engineering, mathematical engineering, physics of
complex systems, mathematics, physics or stochastics
and data science.
Duration of this Projects: 5-6 months
How to contact us
Directly by email to: sonia.cannavo@add-for.com
By LinkedIn: linkedin.com/in/cannavò-sonia-66a95467
Check these Links before moving on[1] Reinforcement Learning:An Introduction
http://incompleteideas.net/book/the-book.html
[2] Deep-Q-Network
https://www.nature.com/articles/nature14236
[3] AlphaGo
https://deepmind.com/research/alphago/
[4] AlphaStar https://bit.ly/2B5YrKh
[5] RL frameworks:
https://github.com/deepmind/trfl
https://github.com/facebookresearch/Horizon
https://github.com/google/dopamine
https://github.com/NervanaSystems/coach
https://github.com/openai/spinningup
[6] OpenAI gym
https://gym.openai.com/
[7] Mujoco
http://mujoco.org/
[8] CARLA Simulator
https://github.com/carla-simulator/carla](https://image.slidesharecdn.com/thesis15-comparisonofreinforcementlearningframeworks-190404041205/85/Master-s-Thesis-comparison-of-reinforcement-learning-frameworks-1-320.jpg)
Master's Thesis - comparison of reinforcement learning frameworks
- 1. MASTERTHESISPROPOSALComparison of Reinforcement Learning Frameworks MASTERTHESISPROPOSAL Reinforcement Learning (RL) is a class of machine learning algorithms in which an agent interacts by trial-and-error in an environment. RL in conjunction with Deep Learning has obtained outstanding results in Atari video games, the Go board-game and a more complex environment like StarCraft II. Recently many open source RL frameworks has been released by software companies in order to easily train and test new RL algorithms. The goal of the thesis is to benchmark the most promising RL frameworks, to study the new algorithms proposed and to evaluate their performance on research environments. Planned Activities 1.Acquire strong theoretical basis on Deep Reinforcement Learning; 2.Install and compare the different RL frameworks; 3.Adapt and apply the best framework to a real application. Required Skills: • Experience with Linux or Unix based OS; • Proficiency in at least one programming language (Python, Lua, Matlab, C++, Java); • Basic knowledge of machine learning; • Good knowledge of linear algebra. Competencies to be acquired: • Experience with the application of Machine Learning to complex systems. • Expertise on the most recent Deep Reinforcement Learning algorithms; • Proficient use of the most advanced development frameworks with a software engineering approach. add-for.com Who we’re looking for Students that are about to get their Master Degree in: computer science, computer engineering, mechatronic engineering, mathematical engineering, physics of complex systems, mathematics, physics or stochastics and data science. Duration of this Projects: 5-6 months How to contact us Directly by email to: sonia.cannavo@add-for.com By LinkedIn: linkedin.com/in/cannavò-sonia-66a95467 Check these Links before moving on[1] Reinforcement Learning:An Introduction http://incompleteideas.net/book/the-book.html [2] Deep-Q-Network https://www.nature.com/articles/nature14236 [3] AlphaGo https://deepmind.com/research/alphago/ [4] AlphaStar https://bit.ly/2B5YrKh [5] RL frameworks: https://github.com/deepmind/trfl https://github.com/facebookresearch/Horizon https://github.com/google/dopamine https://github.com/NervanaSystems/coach https://github.com/openai/spinningup [6] OpenAI gym https://gym.openai.com/ [7] Mujoco http://mujoco.org/ [8] CARLA Simulator https://github.com/carla-simulator/carla