Reinforcement Learning with Google Colab

• 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

Reinforcement learning is a powerful branch of machine learning where agents learn optimal actions by interacting with an environment. This course introduces participants to advanced reinforcement learning algorithms and their implementation using Google Colab. Participants will work with popular libraries such as TensorFlow and OpenAI Gym to create intelligent agents capable of decision-making tasks in dynamic environments.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of reinforcement learning and its practical applications in AI development using Google Colab.

By the end of this training, participants will be able to:

• Understand the core concepts of reinforcement learning algorithms.
• Implement reinforcement learning models using TensorFlow and OpenAI Gym.
• Develop intelligent agents that learn through trial and error.
• Optimize agents' performance using advanced techniques such as Q-learning and deep Q-networks (DQNs).
• Train agents in simulated environments using OpenAI Gym.
• Deploy reinforcement learning models for real-world applications.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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