Reinforcement Learning and Decision Optimization Virtual Internship
In this virtual internship, students will explore the world of reinforcement learning and its applications in decision-making, resource allocation, and optimization problems. They will gain hands-on experience in building intelligent agents and solving complex real-world challenges. Through a series of interactive modules and projects, students will learn to design and implement reinforcement learning algorithms, apply them to various domains, and gain insights into the latest advancements in this rapidly evolving field.
Track Overview
Tasks & Milestones
Implement a Simple Reinforcement Learning Agent
AdvancedIn this task, students will implement a basic reinforcement learning agent and apply it to a simple environment, such as the OpenAI Gym CartPole-v0 environment.
Implement a Deep Q-Network for Atari Game
AdvancedIn this task, students will implement a Deep Q-Network (DQN) agent and train it to play an Atari game from the OpenAI Gym library.
Optimize Resource Allocation using Reinforcement Learning
AdvancedIn this task, students will apply reinforcement learning to solve a resource allocation problem, such as optimizing the distribution of resources across multiple facilities or departments.
Implement a Multi-Agent Reinforcement Learning System
AdvancedIn this task, students will design and implement a multi-agent reinforcement learning system to solve a complex, collaborative problem.
Prerequisites
- • Proficiency in Python programming
- • Strong background in linear algebra, calculus, and probability
- • Familiarity with machine learning concepts and algorithms
Certificate
Certificate of Completion
Earn a certificate upon successful completion