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.

What you will learn?

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.