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Duration 14 hours
Course Outline
Introduction to Reinforcement Learning
- Overview of reinforcement learning and its practical applications
- Distinguishing between supervised, unsupervised, and reinforcement learning
- Key concepts: agent, environment, rewards, and policy
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: The REINFORCE algorithm
- Actor-Critic frameworks and their 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
- Exploring 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
- Addressing sparse rewards and credit assignment problems
- Managing scalability and computational constraints in RL
Hands-On Activities
- Implementing Q-Learning and SARSA algorithms from the ground up
- Training a DQN-based agent to play a simple game in OpenAI Gym
- Fine-tuning RL models for enhanced performance in custom environments
Summary and Next Steps
Requirements
- A solid grasp of machine learning principles and algorithms
- Proficiency in Python programming
- Familiarity with neural networks and deep learning frameworks
Target Audience
- Machine learning engineers
- AI specialists
