Reinforcement Learning for AI Agents Training Course
Reinforcement Learning (RL) stands as a pivotal element in contemporary AI research and applications. It centers on equipping agents with the ability to make optimal decisions within dynamic, multi-step environments.
This instructor-led training session, conducted either online or at your premises, is designed for advanced AI professionals seeking to master reinforcement learning techniques and apply them to train AI agents in tackling complex issues.
Upon completion of this training, participants will be able to:
- Grasp the fundamental principles of reinforcement learning and Markov Decision Processes (MDPs).
- Create and execute RL algorithms including Q-Learning, SARSA, and Deep Q-Networks (DQN).
- Leverage frameworks such as OpenAI Gym and RL libraries for practical use cases.
- Train AI agents to resolve real-world, multi-step decision-making challenges.
- Overcome issues like the exploration-exploitation dilemma and convergence in RL training.
Course Format
- Engaging lectures and discussions.
- Numerous exercises and practice sessions.
- Hands-on implementation within a live-lab setting.
Customization Options for the Course
- To arrange a customized training session, please contact us to discuss your requirements.
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Course Outline
Introduction to Reinforcement Learning
- Overview of reinforcement learning and its applications
- Differences between supervised, unsupervised, and reinforcement learning
- Key concepts: agent, environment, rewards, and policy
Markov Decision Processes (MDPs)
- Understanding states, actions, rewards, and state transitions
- Value functions and the Bellman Equation
- Dynamic programming for solving MDPs
Core RL Algorithms
- Tabular methods: Q-Learning and SARSA
- Policy-based methods: REINFORCE algorithm
- Actor-Critic frameworks and their applications
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
- Introduction to OpenAI Gym and other RL environments
- Using PyTorch or TensorFlow for RL model development
- Training, testing, and benchmarking RL agents
Challenges in RL
- Balancing exploration and exploitation in training
- Dealing with sparse rewards and credit assignment problems
- Scalability and computational challenges in RL
Hands-On Activities
- Implementing Q-Learning and SARSA algorithms from scratch
- Training a DQN-based agent to play a simple game in OpenAI Gym
- Fine-tuning RL models for improved performance in custom environments
Summary and Next Steps
Requirements
- Strong understanding of machine learning principles and algorithms
- Proficiency in Python programming
- Familiarity with neural networks and deep learning frameworks
Audience
- Machine learning engineers
- AI specialists
14 Hours
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