The E-Commerce Re-ranking with Bandits is a reinforcement learning project that trains an agent to make sequential decisions using Synthetic or logged interaction data and Reward simulator, implemented with Python 3.11+. The project focuses on a clean formulation of the environment, reward function, and policy, with careful logging and visualization so learning behaviour is easy to inspect. It is a complete RL capstone that demonstrates both algorithm fundamentals and practical training skills.
Many decision-making tasks are sequential: the best action depends on past states and future consequences, making rule-based or one-shot solutions ineffective. Without a reinforcement learning approach built on Reward simulator and Python 3.11+, agents cannot learn from trial and error, adapt to dynamic conditions, or balance short-term gains against long-term goals.
This project models the task as a Markov Decision Process and trains an agent with Python 3.11+ using Synthetic or logged interaction data. The reward function is carefully designed, and Online policy serving provides insight into the learned policy. The agent improves over time through exploration and exploitation, and the whole pipeline is reproducible and easy to tune.
Python 3.11+
Contextual bandit library
OpenAI Gym / custom environments
Experience replay and target networks
Reproducible training configuration
NumPy / Pandas
Clean environment and reward formulation with Synthetic or logged interaction data
Configurable algorithm and training hyperparameters
Episode and reward logging with learning curves
Policy evaluation and visualization
Reproducible experiments
Extensible architecture for new environments
The project separates the environment from the agent. The environment implements states, actions, and rewards through Synthetic or logged interaction data; the agent maintains a policy learned with Python 3.11+ and Reward simulator; and the training loop coordinates episodes, logging, and evaluation. A configuration module controls all hyperparameters, and a visualization module renders learning curves and sample rollouts.
Set up the Python environment and define the task as an MDP with Synthetic or logged interaction data.
Implement the environment interface, state, action, and reward functions.
Build the learning algorithm with Python 3.11+ and Reward simulator.
Train the agent and log episode rewards, tuning exploration and learning rate.
Evaluate the learned policy across multiple seed runs.
Add visualization and export the trained policy.
Write documentation and prepare the demo and viva report.
Understand Markov Decision Processes and reward design
Implement and debug core RL algorithms
Apply Multi-armed and contextual bandits and Exploration-exploitation trade-offs
Analyze learning curves and convergence
Present and defend a complete RL project in viva
Scale to a more complex environment or real hardware
Add imitation learning to bootstrap from demonstrations
Deploy the policy behind an API for live interaction
Add model-based planning for sample efficiency
The E-Commerce Re-ranking with Bandits delivers a complete reinforcement learning workflow — environment design, algorithm implementation, training, and evaluation. It is practical, reproducible, and easy to explain, making it an excellent final year project that showcases real understanding of modern AI.
