Reinforcement Learning
Master the Future of AI with Advanced Reinforcement Learning Techniques.
Early access to e-LMS included
About This Course
This Program is designed to provide a comprehensive understanding of reinforcement learning (RL) and its applications. Participants will explore the foundational principles of RL, including Markov decision processes and dynamic programming. The course will delve into advanced topics such as deep Q-learning, policy gradients, and proximal policy optimization (PPO). By the end of the course, participants will be proficient in using key RL libraries and frameworks, preparing them for advanced studies or careers in reinforcement learning and AI.
Program Objectives
- Understand and apply foundational reinforcement learning concepts.
- Explore and implement Markov decision processes (MDP) and dynamic programming techniques.
- Develop and optimize RL algorithms including deep Q-learning, policy gradients, and PPO.
- Utilize Monte Carlo methods and temporal-difference learning for model training.
- Gain hands-on experience with deep reinforcement learning using TensorFlow and PyTorch.
- Apply RL techniques to solve real-world problems and build advanced AI applications.
- Complete projects that demonstrate practical RL skills and knowledge.
- Prepare for advanced roles in reinforcement learning and AI through comprehensive training and hands-on practice.
Program Structure
Introduction to Reinforcement Learning:
- Overview of Reinforcement Learning.
- Key Concepts and Terminologies.
- Applications and Use Cases.
Markov Decision Processes (MDP):
- Understanding MDPs.
- States, Actions, and Rewards.
- Policy and Value Functions.
Dynamic Programming:
- Bellman Equations.
- Value Iteration.
- Policy Iteration.
Monte Carlo Methods:
- Monte Carlo Prediction.
- Monte Carlo Control.
- Off-Policy Methods.
Temporal-Difference Learning:
- TD Prediction.
- SARSA (State-Action-Reward-State-Action).
- Q-Learning.
Deep Reinforcement Learning:
- Deep Q-Networks (DQN).
- Double DQN and Dueling DQN.
- Policy Gradients and Actor-Critic Methods.
- Proximal Policy Optimization (PPO).
Advanced Topics:
- Multi-Agent Reinforcement Learning.
- Hierarchical Reinforcement Learning.
- Inverse Reinforcement Learning.
- Safety and Ethics in RL.
Practical Implementation:
- Using OpenAI Gym for Simulation Environments.
- Implementing RL Algorithms with TensorFlow and PyTorch.
- Building and Deploying RL Models.
Who Should Enrol?
- Senior undergraduates and graduate students in Computer Science and related fields.
- Professionals in IT, data science, and software development looking to enhance their RL skills.
Program Outcomes
- Develop a strong understanding of reinforcement learning principles and techniques.
- Gain proficiency in Markov decision processes and dynamic programming.
- Implement and optimize RL algorithms such as deep Q-learning, policy gradients, and PPO.
- Master the use of key RL libraries and frameworks including OpenAI Gym, TensorFlow, and PyTorch.
- Apply RL concepts to real-world projects and scenarios.
- Enhance Python programming skills for advanced RL tasks.
- Complete practical coding exercises and projects demonstrating RL expertise.
- Earn a certificate of completion recognized by industry leaders.
Fee Structure
Discounted: ₹10,999 | $164
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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