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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
- Achieving the balance between exploration and exploitation in RL models.
- Exploration strategies: epsilon-greedy, softmax, and others.
Q-Learning and Deep Q-Networks (DQNs)
- Introduction to Q-learning.
- Implementing DQNs using TensorFlow.
- Enhancing Q-learning with experience replay and target networks.
Policy-Based Methods
- Policy gradient algorithms.
- The REINFORCE algorithm and its implementation.
- Actor-critic architectures.
Working with OpenAI Gym
- Configuring environments within OpenAI Gym.
- Simulating agent behavior in dynamic environments.
- 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.
- A foundational understanding of deep learning and machine learning principles.
- Familiarity with the algorithms and mathematical theories integral to reinforcement learning.
Target Audience
- Data scientists.
- Machine learning engineers and practitioners.
- Artificial intelligence researchers.
28 Hours
