The Sequential Recommendation with Q-Learning is a reinforcement learning project that trains an agent to make sequential decisions using Contextual features and Online policy serving, implemented with Contextual bandit library. 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 Online policy serving and Contextual bandit library, 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 Contextual bandit library using Contextual features. The reward function is carefully designed, and Synthetic or logged interaction data 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.
Contextual bandit library
A/B test simulator
Python 3.11+
OpenAI Gym / custom environments
Experience replay and target networks
Reproducible training configuration
NumPy / Pandas
Clean environment and reward formulation with Contextual features
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 Contextual features; the agent maintains a policy learned with Contextual bandit library and Online policy serving; 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 Contextual features.
Implement the environment interface, state, action, and reward functions.
Build the learning algorithm with Contextual bandit library and Online policy serving.
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 Thompson sampling and UCB and Regret and reward evaluation
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 Sequential Recommendation with Q-Learning 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.
