Reinforcement Learning for AI Agents Training in India

Participants will learn how agents learn optimal behavior through interaction with environments and rewards.

Reinforcement Learning for AI Agents Training is a professional training program delivered by ProgNXT, a globally recognized corporate training provider. ProgNXT's Reinforcement Learning for AI Agents Training course in India equips professionals with industry-relevant skills through hands-on, instructor-led sessions. This course provides an in-depth introduction to Reinforcement Learning (RL) with a strong emphasis on designing intelligent, goal-driven AI agents....

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: FAY45
14 Hrs
Course Rating 4.6/5
Last Updated: April 2026

This course provides an in-depth introduction to Reinforcement Learning (RL) with a strong emphasis on designing intelligent, goal-driven AI agents. Participants will learn how agents learn optimal behavior through interaction with environments and rewards. The course blends theory, algorithms and hands-on programming using Python and RLlib. Ideal for professionals aiming to build agents for robotics, game AI, automation, or autonomous decision systems.

Welcome to the official Reinforcement Learning for AI Agents Training certification program. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge in in India. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 14 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Solid understanding of Python programming
Basic knowledge of machine learning concepts (supervised/unsupervised learning)
Familiarity with linear algebra, probability, and optimization
(Optional) Prior experience with libraries like PyTorch or TensorFlow

Designing reward functions and action policies
Implementing value-based and policy-based algorithms
Using OpenAI Gym, Stable-Baselines3, RLlib
Applying RL in simulations and real-time scenarios
Hyperparameter tuning and convergence strategies

After completing this course, participants will be able to:

Understand the fundamentals of reinforcement learning and its role in building intelligent agents
Implement key RL algorithms from scratch and via libraries
Design environments and reward structures
Train, evaluate, and tune RL-based AI agents
Apply RL to real-world or simulated tasks (e.g., navigation, scheduling, game AI)

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