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Duration 28 hours
Course Outline
Introduction to Reinforcement Learning
- Defining reinforcement learning.
- Core concepts: agents, environments, states, actions, and rewards.
- Key challenges in reinforcement learning.
Exploration and Exploitation
- Balancing exploration and exploitation within RL models.
- Exploration strategies: epsilon-greedy, softmax, and others.
Q-Learning and Deep Q-Networks (DQNs)
- Introduction to Q-learning.
- Implementing DQNs with TensorFlow.
- Optimizing Q-learning through experience replay and target networks.
Policy-Based Methods
- Policy gradient algorithms.
- The REINFORCE algorithm and its implementation.
- Actor-critic methodologies.
Working with OpenAI Gym
- Configuring environments in OpenAI Gym.
- Simulating agents in dynamic settings.
- Assessing 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
- Proficiency in Python programming
- Foundational knowledge of deep learning and machine learning concepts
- Familiarity with the algorithms and mathematical frameworks underpinning reinforcement learning
Audience
- Data scientists
- Machine learning professionals
- AI researchers
