Reinforcement Learning for AI Agents Training Course
Reinforcement Learning (RL) serves as a fundamental pillar in contemporary artificial intelligence research and practical deployment. It centers on developing agents capable of making optimal decisions within dynamic, multi-stage environments.
Delivered by expert instructors, this live training program—available both online and on-site—is tailored for senior AI professionals aiming to refine their mastery of reinforcement learning techniques and apply them to train AI agents for complex problem-solving scenarios.
Upon completion, participants will be equipped to:
- Grasp the foundational principles of reinforcement learning and Markov Decision Processes (MDPs).
- Architect and deploy RL algorithms, including Q-Learning, SARSA, and Deep Q-Networks (DQN).
- Leverage robust frameworks such as OpenAI Gym and specialized RL libraries for real-world applications.
- Guide AI agents in navigating and resolving intricate, multi-step decision-making challenges.
- Navigate critical issues such as the exploration-exploitation trade-off and training convergence in RL systems.
Training Structure
- Engaging lectures paired with interactive discussions.
- Extensive exercises focused on practical application.
- Live-lab sessions for hands-on implementation.
Tailored Training Options
- To discuss customizing this curriculum for specific organizational needs, please reach out to our team.
Course Outline
Fundamentals of Reinforcement Learning
- A broad overview of reinforcement learning and its diverse applications
- Distinguishing between supervised, unsupervised, and reinforcement learning approaches
- Core concepts: agent, environment, reward signals, and policy
Markov Decision Processes (MDPs)
- Analyzing states, actions, rewards, and state transitions
- Exploring value functions and the Bellman Equation
- Applying dynamic programming techniques to solve MDPs
Essential RL Algorithms
- Tabular methods: Implementing Q-Learning and SARSA
- Policy-based approaches: The REINFORCE algorithm
- Actor-Critic architectures and their practical uses
Deep Reinforcement Learning
- Overview of Deep Q-Networks (DQN)
- Mechanisms of experience replay and target networks
- Policy gradients and sophisticated deep RL methodologies
RL Frameworks and Toolsets
- Getting started with OpenAI Gym and other RL environments
- Building RL models using PyTorch or TensorFlow
- Procedures for training, testing, and benchmarking RL agents
Navigating RL Challenges
- Striking the balance between exploration and exploitation during training
- Handling sparse rewards and credit assignment dilemmas
- Addressing scalability and computational constraints in RL
Practical Workshops
- Coding Q-Learning and SARSA algorithms from the ground up
- Training a DQN-based agent to play simple games within OpenAI Gym
- Optimizing RL models for enhanced performance in customized environments
Wrap-up and Future Directions
Requirements
- A solid command of core machine learning principles and algorithms
- Advanced proficiency in Python programming
- Working knowledge of neural networks and deep learning frameworks
Target Audience
- Machine learning engineers
- Specialists in artificial intelligence
Open Training Courses require 5+ participants.
Reinforcement Learning for AI Agents Training Course - Booking
Reinforcement Learning for AI Agents Training Course - Enquiry
Reinforcement Learning for AI Agents - Consultancy Enquiry
Provisional Upcoming Courses (Require 5+ participants)
Related Courses
Agentic Development with Gemini 3 and Google Antigravity
21 HoursGoogle Antigravity serves as an agentic development environment, enabling the creation of autonomous agents that can plan, reason, code, and execute actions through the multimodal capabilities of Gemini 3.
This instructor-led live training, available online or onsite, is designed for advanced technical professionals who want to design, build, and deploy autonomous agents using Gemini 3 within the Antigravity environment.
Upon completing this training, participants will be equipped to:
- Create autonomous workflows that leverage Gemini 3 for reasoning, planning, and execution.
- Develop agents in Antigravity capable of analyzing tasks, writing code, and interacting with various tools.
- Integrate agents powered by Gemini into enterprise systems and APIs.
- Enhance agent behavior, safety, and reliability in complex operational environments.
Course Format
- Expert demonstrations paired with interactive discussions.
- Hands-on experimentation focused on autonomous agent development.
- Practical implementation utilizing Antigravity, Gemini 3, and supporting cloud tools.
Customization Options
- For teams requiring domain-specific agent behaviors or custom integrations, please contact us to tailor the program.
Advanced Antigravity: Feedback Loops, Learning & Long-Term Agent Memory
14 HoursGoogle Antigravity serves as a sophisticated framework for experimenting with persistent agents and emergent interactive behaviors.
This instructor-led live training (available online or onsite) is designed for advanced professionals aiming to design, analyze, and optimize agents that retain memories, improve via feedback, and evolve over extended operational periods.
After completing this course, participants will acquire the skills to:
- Construct memory architectures for agent persistence.
- Deploy effective feedback loops to influence agent behavior.
- Assess learning trajectories and monitor model drift.
- Integrate memory mechanisms into complex multi-agent environments.
Course Format
- Expert-led discussions combined with technical demonstrations.
- Practical exploration through structured design challenges.
- Application of concepts within simulated agent environments.
Course Customization Options
- For organizations requiring tailored content or specific case examples, please reach out to customize this training.
Advanced Mastra Integrations: APIs, Tools, Enterprise Data & External Systems
21 HoursMastra is a framework designed to facilitate deep integration between AI agents, APIs, enterprise applications, and external data systems.
This instructor-led live training (available online or onsite) targets intermediate-level engineers looking to build reliable, secure, and scalable integrations between Mastra agents and the wider enterprise ecosystem.
Upon completing this training, participants will be equipped to:
- Implement API-driven integrations connecting Mastra agents with external services.
- Link enterprise data systems and tools to automated agent workflows.
- Apply best practices for secure data exchange and authentication.
- Design integration layers that are scalable, maintainable, and ready for production use.
Course Format
- Interactive lectures and discussions.
- Hands-on engineering exercises involving integrations and APIs.
- Live laboratory implementation using real-world enterprise scenarios.
Course Customization Options
- Custom API scenarios, enterprise system mappings, or data-integration workshops can be arranged upon request.
Interactive AI Agents: AgentCore Memory, Code Interpreter & Browser Tool in Action
14 HoursAgentCore empowers AI agents to create dynamic, interactive, and context-aware experiences by offering memory persistence, a secure code interpreter, and a browser tool.
This live, instructor-led training (available online or onsite) is designed for technical practitioners at intermediate to advanced levels who aim to build and deploy AI agents that retain long-term context, perform on-the-fly calculations, and interact directly with web interfaces.
Upon completion, participants will be equipped to:
- Implement AgentCore memory for workflows that are stateful and context-aware.
- Utilize the secure code interpreter for performing dynamic calculations and data transformations.
- Integrate the browser tool to facilitate real-time data retrieval and UI interactions.
- Design interactive agents for applications in analytics, customer support, and research.
Course Format
- Engaging lectures and open discussions.
- Practical lab exercises utilizing AgentCore memory and tools.
- Case studies focusing on analytics, automation, and customer support scenarios.
Course Customization
- Contact us to arrange a customized training session for this course.
Accelerating AI Agent Deployment with AgentCore Runtime & Gateway
14 HoursAgentCore Runtime & Gateway serves as a paired AWS service designed to streamline the packaging, deployment, and secure exposure of AI agents, facilitating seamless integration with external systems.
This instructor-led training session, available online or onsite, targets intermediate-level engineering teams aiming to transition their agent prototypes into production. The curriculum focuses on mastering the AgentCore Runtime for deployment and the Gateway for secure connectivity and API integration.
Upon completion of this course, participants will be capable of:
- Establishing AgentCore Runtime environments and packaging agents for deployment.
- Exposing agents via the Gateway using authenticated endpoints with rate limiting.
- Integrating external tools and APIs into agent workflows through stable contracts.
- Implementing observability, logging, and usage monitoring essential for production operations.
Course Format
- Interactive lectures and discussions.
- Practical labs featuring Runtime deployments and Gateway integrations.
- Targeted exercises emphasizing reliability, security, and deployment strategies.
Customization Options
- To arrange customized training for this course, please contact us.
Antigravity for Developers: Building Agent-First Applications
21 HoursAntigravity serves as a development platform specifically designed to create AI-driven, agent-first applications.
This instructor-led live training, available both online and onsite, targets intermediate developers aiming to build real-world applications leveraging autonomous AI agents within the Antigravity ecosystem.
Upon completing this training, participants will be able to:
- Develop applications that depend on coordinated and autonomous AI agents.
- Utilize the Antigravity IDE, including its editor, terminal, and browser features, for end-to-end development workflows.
- Manage multi-agent workflows effectively using the Agent Manager.
- Integrate agent capabilities into robust, production-grade software systems.
Course Format
- A blend of presentations and in-depth practical demonstrations.
- Extensive hands-on practice accompanied by guided exercises.
- Real-world implementation tasks conducted within the live Antigravity environment.
Course Customization Options
- For content tailored to align with your specific development stack, please contact us to arrange a customized training session.
Getting Started with Antigravity: An Introduction to Agent-First IDEs
14 HoursGoogle Antigravity is an agent-first development environment designed to streamline engineering workflows through intelligent automation.
This instructor-led, live training (online or onsite) is aimed at beginner-level practitioners who wish to explore the fundamentals of Antigravity and understand how agent-driven coding environments enhance productivity.
Upon completion of this training, participants will be able to:
- Install and configure Google Antigravity.
- Navigate and understand both the Editor View and Manager View.
- Work effectively with agents to automate simple development tasks.
- Use Antigravity to generate, refine, and manage project files.
Format of the Course
- Instructor explanations supported by real-time demonstrations.
- Guided exercises focused on hands-on use of agents.
- Practical exploration of core Antigravity features in a controlled lab environment.
Course Customization Options
- If you require a tailored version of this training, please contact us to arrange a customized program.
Antigravity for Web Automation & Browser-Based Tasks
21 HoursGoogle Antigravity serves as a platform for developing agents that interact with web applications, browser environments, and multi-surface workflows.
This instructor-led live training, available online or onsite, is designed for intermediate-level professionals looking to build, automate, and test browser-based workflows using Google Antigravity.
After completing the training, participants will be able to:
- Create agents that interact with web applications via a browser interface.
- Automate end-to-end workflows across various browser contexts.
- Validate and troubleshoot agent behavior in user interface-driven environments.
- Implement cross-surface automation strategies using Antigravity.
Format of the Course
- Guided instruction supported by demonstrations.
- Practical, hands-on activities and scenario-based exercises.
- Implementation of agent workflows in an interactive lab environment.
Course Customization Options
- For customized training requirements, please contact us to tailor the course to your objectives.
Building Fully Managed AI Agents with AgentCore: From Concept to Production
14 HoursAgentCore streamlines the development, optimization, and monitoring of fully managed AI agents by offering a comprehensive suite of services designed for large-scale deployment.
This instructor-led live training, available online or on-site, is designed for practitioners ranging from beginners to intermediates who seek practical experience in building production-ready AI agents using AgentCore.
Upon completing this training, participants will be equipped to:
- Grasp the fundamental capabilities of AgentCore in AI agent development.
- Architect and set up simple AI agents utilizing managed services.
- Incorporate workflows to augment agent functionality.
- Release and oversee AI agents within production environments.
Course Format
- Engaging lectures and interactive discussions.
- Practical labs focused on AgentCore services.
- Structured exercises guiding the journey from concept to deployment.
Customization Options
- For tailored training requests regarding this course, please reach out to us to coordinate the arrangement.
AI Agent Development with Mastra
14 HoursThis live, instructor-led training, offered online or onsite, is designed for intermediate-level developers and engineering teams looking to build scalable, observable AI systems using Mastra.
By the end of the session, learners will be able to:
- Understand Mastra's architecture and how it connects with LLMs and external APIs.
- Design and build AI agents and workflows in TypeScript.
- Utilize Mastra's observability and memory tools to track and optimize agent performance.
- Deploy production-ready AI applications using Mastra's framework features.
Mastra Debugging, Evaluation & Quality Assurance for AI Agents
21 HoursMastra is a framework that offers structured tools to evaluate, debug, and ensure the reliability of AI agents operating within complex workflows.
This instructor-led, live training (available online or onsite) targets intermediate-level practitioners who want to rigorously test agent behavior, enhance reliability, and implement measurable evaluation processes.
By the end of this training, participants will be able to confidently:
- Apply debugging techniques to identify and resolve agent behavior issues.
- Evaluate agents using structured metrics, benchmarks, and quality scores.
- Implement tooling and workflows that track reliability, drift, and hallucinations.
- Design QA strategies that ensure consistent and predictable agent performance.
Course Format
- Interactive lectures and discussions.
- Hands-on debugging and evaluation exercises.
- Live-lab analysis of agent behaviors using observability tools.
Course Customization Options
- Customized reliability testing scenarios and industry-specific QA methods can be arranged upon request.
Mastra Ops & Production Engineering: Deploying and Scaling AI Agents
21 HoursMastra is an operational framework designed to streamline the deployment, scaling, and lifecycle management of AI agents in production environments.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level technical professionals who need to operationalize AI agents reliably and efficiently across production systems.
Upon completion of this training, attendees will be equipped to:
- Deploy Mastra-based AI agents into controlled, production-grade environments.
- Scale agents horizontally and vertically using platform-native primitives.
- Implement observability pipelines to track agent behaviour and performance.
- Optimize runtime configurations to reduce latency, costs, and operational risks.
Format of the Course
- Interactive lecture and discussion.
- Hands-on exercises focused on real deployment scenarios.
- Live-lab implementation using containerized and orchestrated environments.
Course Customization Options
- Customization of topics, hands-on labs, or industry-specific scenarios is available upon request.
Mastra Workflow Automation & Multi-Agent Orchestration
21 HoursMastra is a framework that empowers sophisticated workflow automation and coordination across multiple AI agents operating within distributed systems. <\/p>
This instructor-led training session (available online or onsite) targets intermediate-level practitioners who aim to design, orchestrate, and manage multi-agent workflows at scale. <\/p>
Upon completion of this training, participants will acquire the ability to: <\/p>
- Design complex workflows utilizing Mastra’s orchestration capabilities. <\/li>
- Coordinate multiple agents executing parallel or dependent tasks. <\/li>
- Deploy monitoring and debugging tools for efficient workflow execution. <\/li>
-
Optimize orchestration logic to enhance reliability, throughput, and automation efficiency.
<\/li>
<\/ul>
Course Format <\/p>
- Interactive lectures and discussions. <\/li>
- Hands-on exercises focused on workflow design and automation. <\/li>
-
Practical implementation within a containerized live-lab environment.
<\/li>
<\/ul>
Customization Options <\/p>
- Custom automation scenarios, enterprise integrations, or workflow patterns can be tailored upon request. <\/li> <\/ul>
Managing Agent Workflows in Google Antigravity: Orchestration, Planning and Artifacts
14 HoursGoogle Antigravity serves as a platform centered on agents, designed to orchestrate, oversee, and coordinate workflows for AI-driven coding and automation.
This instructor-led training session, available either online or at your facility, targets intermediate-level professionals seeking to design, manage, and optimize multi-agent workflows within the Google Antigravity ecosystem.
Upon completing this training, participants will be able to:
- Set up agent responsibilities and orchestration pipelines through the Manager interface.
- Create and interpret Antigravity artifacts such as task lists, plans, logs, and browser recordings.
- Establish verification strategies to maintain transparency and auditability in agent actions.
- Enhance multi-agent collaboration for intricate development and operational assignments.
Course Format
- Guided presentations combined with practical demonstrations.
- Scenario-based exercises targeting real-world workflow challenges.
- Hands-on exploration within a live Antigravity workspace.
Customization Options
- For those needing a customized version of this course, please reach out to discuss specific customization possibilities.
Testing & Verifying Agent-Driven Code: Quality Assurance in Antigravity
14 HoursAntigravity is a framework designed to represent sophisticated, agent-driven development workflows.
This instructor-led live training (available online or onsite) targets intermediate to advanced professionals seeking to verify, validate, and secure the outputs produced by AI agents operating within Antigravity environments.
After completing this course, participants will be capable of:
- Evaluating the accuracy and safety of code artifacts generated by agents.
- Employing structured methods to verify tasks executed by agents.
- Effectively analyzing browser recordings and tracing agent activities.
- Applying QA and security principles to guarantee the reliability of agent workflows.
Course Format
- Technical briefings and discussions guided by an instructor.
- Practical exercises focused on verifying real-world agent workflows.
- Hands-on testing and validation within a controlled lab environment.
Customization Options
- Scenarios, workflows, and testing examples can be adapted upon request.
