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Duration 28 hours
Course Outline
Introduction to Reinforcement Learning
- Defining reinforcement learning.
- Essential concepts: agents, environments, states, actions, and rewards.
- Common challenges in reinforcement learning.
Exploration versus Exploitation
- Managing the balance between exploration and exploitation in RL models.
- Exploration strategies: epsilon-greedy, softmax, and others.
Q-Learning and Deep Q-Networks (DQNs)
- Overview of Q-learning.
- Building DQNs using TensorFlow.
- Enhancing Q-learning with experience replay and target networks.
Policy-Based Approaches
- Policy gradient algorithms.
- The REINFORCE algorithm and its practical implementation.
- Actor-critic methodologies.
Utilizing OpenAI Gym
- Configuring environments within OpenAI Gym.
- Simulating agent behavior 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
- Practical applications of reinforcement learning.
- Integrating RL models into production systems.
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Foundational knowledge of deep learning and machine learning principles
- Familiarity with the algorithms and mathematical frameworks essential to reinforcement learning
Target Audience
- Data scientists
- Machine learning engineers and practitioners
- Artificial intelligence researchers
