Reinforcement Learning : Algorithms, Applications & Frontiers
Mastering Intelligent Decision-Making Through Reinforcement Learning!
About This Course
Reinforcement Learning (RL) is a key area of machine learning where agents learn optimal actions by maximizing cumulative rewards in an environment. It lies at the heart of AI systems capable of autonomous learning and decision-making. This workshop combines theory, coding, and cutting-edge applications, enabling participants to develop intelligent systems powered by RL.
Aim
The Reinforcement Learning (RL) Workshop is designed to introduce learners to the core principles, algorithms, practical applications, and emerging frontiers of reinforcement learning. Participants will understand how RL agents learn from interactions, optimize performance through feedback, and apply these methods to domains like robotics, gaming, finance, healthcare, and AI research.
Workshop Objectives
- To provide a strong conceptual and practical foundation in reinforcement learning
- To teach key RL algorithms and their variants, with real-world implementation
- To explore applications of RL across industries and domains
- To introduce advanced methods and current frontiers in RL research
- To highlight ethical concerns, safety, and AI alignment issues in RL systems
Workshop Structure
🧠 Day 1: RL Foundations & Algorithms
- RL Concepts: Agents, environments, rewards, policies
- Key Algorithms: Q-Learning, DQN, A3C, PPO
- RL vs. Supervised & Unsupervised Learning
Live Activities: - RL agent demo (OpenAI Gym + Colab)
- Interactive quiz (Kahoot/Slido)
🌍 Day 2: Real-World Applications
- Use Cases: Robotics, finance, healthcare, gaming, recommendations
- Deployment Challenges: Efficiency, safety, interpretability
- Industry Case Studies: Uber, Netflix, DeepMind
Live Activities: - Simulation: Stock trading / warehouse RL task
- Guest expert session + Q&A
🔬 Day 3: Tools & Future Frontiers
- Tools: Ray RLlib, TensorFlow Agents, PyTorch RL, Unity ML-Agents
- Advanced Topics: Offline RL, Multi-Agent RL, Safe RL, LLM integration
- Domains: Climate, edge AI, quantum computing
Live Activities: - RL training demo (Ray RLlib + Colab)
- Open forum: Trends, research, collaboration
Who Should Enrol?
- Machine Learning & AI Engineers
- Data Scientists & Researchers
- Robotics Developers & Software Engineers
- Quantitative Analysts & Financial Engineers
- Graduate Students in CS, EE, or Math
Important Dates
Registration Ends
04/24/2025
IST 4 PM
Workshop Dates
04/24/2025 – 04/26/2025
IST 6 PM
Workshop Outcomes
✔ Understand theoretical and mathematical underpinnings of reinforcement learning
✔ Be able to implement core and deep RL algorithms from scratch and using frameworks
✔ Gain hands-on experience in training intelligent agents in simulated environments
✔ Explore real-world case studies where RL is making significant impact
✔ Stay informed about the latest trends and open research problems in RL
Fee Structure
Student
₹1999 | $50
Ph.D. Scholar / Researcher
₹2499 | $55
Academician / Faculty
₹2999 | $60
Industry Professional
₹4999 | $85
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
View All Feedbacks →