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Course Outline
Foundations of Reinforcement Learning
- Overview of reinforcement learning and its diverse applications
- Distinguishing between supervised, unsupervised, and reinforcement learning
- Key concepts: agents, environments, rewards, and policies
Markov Decision Processes (MDPs)
- Understanding states, actions, rewards, and state transitions
- Value functions and the Bellman Equation
- Applying dynamic programming to solve MDPs
Core RL Algorithms
- Tabular methods: Q-Learning and SARSA
- Policy-based methods: REINFORCE algorithm
- Actor-Critic frameworks and their practical uses
Deep Reinforcement Learning
- Introduction to Deep Q-Networks (DQN)
- Experience replay and target networks
- Policy gradients and advanced deep RL methodologies
RL Frameworks and Tools
- Introduction to OpenAI Gym and other RL environments
- Using PyTorch or TensorFlow for developing RL models
- Training, testing, and benchmarking RL agents
Navigating Challenges in RL
- Balancing exploration and exploitation during training
- Addressing sparse rewards and credit assignment problems
- Managing scalability and computational demands in RL
Hands-On Workshops
- Building Q-Learning and SARSA algorithms from scratch
- Training a DQN-based agent to play a simple game in OpenAI Gym
- Fine-tuning RL models to enhance performance in custom environments
Wrap-up and Future Directions
Requirements
- A solid command of machine learning principles and algorithms
- Advanced proficiency in Python programming
- Familiarity with neural networks and deep learning frameworks
Target Audience
- Machine learning engineers
- AI specialists
14 Hours
