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Duration 14 hours
Course Outline
Introduction to Reinforcement Learning
- Overview of reinforcement learning and its various applications.
- Distinguishing between supervised, unsupervised, and reinforcement learning.
- Key concepts: agents, environments, rewards, and policies.
Markov Decision Processes (MDPs)
- Analyzing 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 practical uses.
Deep Reinforcement Learning
- Introduction to Deep Q-Networks (DQN).
- Experience replay and target networks.
- Policy gradients and advanced deep RL methods.
RL Frameworks and Tools
- Familiarization with OpenAI Gym and other RL environments.
- Utilizing PyTorch or TensorFlow for developing RL models.
- Processes for training, testing, and benchmarking RL agents.
Challenges in RL
- Balancing exploration and exploitation during training.
- Managing sparse rewards and credit assignment problems.
- Addressing scalability and computational hurdles in RL.
Hands-On Activities
- Implementing Q-Learning and SARSA algorithms from scratch.
- Training a DQN-based agent to play simple games in OpenAI Gym.
- Fine-tuning RL models to enhance performance in custom environments.
Summary and Next Steps
Requirements
- A solid grasp of machine learning principles and algorithms.
- Proficiency in Python programming.
- Knowledge of neural networks and deep learning frameworks.
Target Audience
- Machine learning engineers.
- AI specialists.
