Reinforcement Learning with Google Colab Training Course
Reinforcement learning is a robust 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 develop 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 for government 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.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact Govtra to arrange.
Course Outline
Introduction to Reinforcement Learning for Government
- What is reinforcement learning?
- Key concepts: agent, environment, states, actions, and rewards
- Challenges in reinforcement learning
Exploration and Exploitation for Government
- Balancing exploration and exploitation in RL models
- Exploration strategies: epsilon-greedy, softmax, and more
Q-Learning and Deep Q-Networks (DQNs) for Government
- Introduction to Q-learning
- Implementing DQNs using TensorFlow
- Optimizing Q-learning with experience replay and target networks
Policy-Based Methods for Government
- Policy gradient algorithms
- REINFORCE algorithm and its implementation
- Actor-critic methods
Working with OpenAI Gym for Government
- Setting up environments in OpenAI Gym
- Simulating agents in dynamic environments
- Evaluating agent performance
Advanced Reinforcement Learning Techniques for Government
- Multi-agent reinforcement learning
- Deep deterministic policy gradient (DDPG)
- Proximal policy optimization (PPO)
Deploying Reinforcement Learning Models for Government
- Real-world applications of reinforcement learning
- Integrating RL models into production environments
Summary and Next Steps for Government
Requirements
- Experience with Python programming for government applications
- Basic understanding of deep learning and machine learning concepts for government use cases
- Knowledge of algorithms and mathematical concepts utilized in reinforcement learning for government projects
Audience
- Data scientists working in the public sector
- Machine learning practitioners focused on government initiatives
- AI researchers supporting government projects
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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