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Course Outline
Introduction to Reinforcement Learning
- What is reinforcement learning?
- Key concepts: agent, environment, states, actions, and rewards
- Challenges in reinforcement learning
Exploration and Exploitation
- Balancing exploration and exploitation in RL models
- Exploration strategies: epsilon-greedy, softmax, and more
Q-Learning and Deep Q-Networks (DQNs)
- Introduction to Q-learning
- Implementing DQNs using TensorFlow
- Optimizing Q-learning with experience replay and target networks
Policy-Based Methods
- Policy gradient algorithms
- REINFORCE algorithm and its implementation
- Actor-critic methods
Working with OpenAI Gym
- Setting up environments in OpenAI Gym
- Simulating agents in dynamic environments
- Evaluating agent performance
Advanced Reinforcement Learning Techniques
- Multi-agent reinforcement learning
- Deep deterministic policy gradient (DDPG)
- Proximal policy optimization (PPO)
Deploying Reinforcement Learning Models
- Real-world applications of reinforcement learning
- Integrating RL models into production environments
Summary and Next Steps
Requirements
- Experience with Python programming
- Basic understanding of deep learning and machine learning concepts
- Knowledge of algorithms and mathematical concepts used in reinforcement learning
Audience
- Data scientists
- Machine learning practitioners
- AI researchers
28 Hours
Testimonials (1)
The training level was high. The instructor was not afraid to use mathematical formalisms.
Mateusz Soczewka - Santander Bank Polska S.A.
Course - Fundamentals of Reinforcement Learning
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