INTRODUCTION
The Reinforcement Learning and Deep Learning (RLDL) Lab focuses on providing hands-on experience with RL and DL algorithms through practical implementations. The syllabus covers Markov Decision Processes (MDPs), Q-learning, Policy Gradient Methods, and Deep Q-Networks (DQNs). Students will learn to apply RL algorithms for decision-making in dynamic environments and use DL techniques for classification, regression, and pattern recognition. The lab emphasizes industry-relevant applications, including autonomous navigation and predictive modeling, using libraries such as TensorFlow, and PyTorch.
OBJECTIVE OF THE LABORATORY
The objective of the laboratory is
- To introduce the foundation of Reinforcement learning foundation and Q Network algorithm.
- To understand policy optimization, recent advanced techniques and applications of Reinforcement learning.
- To introduce the concept of deep learning and neural network.
- To understand the concept of NLP and computer vision in deep learning.
FACILITIES
- Software –Anaconda (Open Source)
- Hardware – Computers with 13th Intel Core i9 Processor, 32GB RAM, Operating System windows 10 PRO – 24 Nos.
Printer-HP LASER Jet M1005MFP – 1 No.
STAFF
- Lab Incharge: Mihika Mahendra
- Other Faculty Members: Nil
- Lab Assistant: Mr. Vishwajit Kadam
PHOTOGRAPH OF LAB
