Reinforcement Learning for AI Agents Training Course

Introduction to Reinforcement Learning

• Overview of reinforcement learning and its real-world applications
• Distinguishing between supervised, unsupervised, and reinforcement learning
• Key concepts: agent, environment, rewards, and policy

Markov Decision Processes (MDPs)

• Exploring states, actions, rewards, and state transitions
• Value functions and the Bellman Equation
• Using dynamic programming to solve MDPs

Core RL Algorithms

• Tabular approaches: Q-Learning and SARSA
• Policy-based strategies: The REINFORCE algorithm
• Actor-Critic frameworks and their use cases

Deep Reinforcement Learning

• Getting started with Deep Q-Networks (DQN)
• Experience replay and target networks
• Policy gradients and advanced deep RL techniques

RL Frameworks and Tools

• Introduction to OpenAI Gym and other RL environments
• Developing RL models using PyTorch or TensorFlow
• Training, testing, and benchmarking RL agents

Challenges in RL

• Balancing exploration and exploitation during training
• Managing sparse rewards and credit assignment issues
• Addressing scalability and computational demands in RL

Hands-On Activities

• Building Q-Learning and SARSA algorithms from the ground up
• Training a DQN-based agent to play a simple game in OpenAI Gym
• Optimizing RL models for better performance in custom environments

Summary and Next Steps