# Intelligent & Learning Agents

> Bandit algorithms, MDP solvers, and a Minimax chess agent.

- Year: 2025
- Period: August–November 2025
- Type: course
- Stack: Python, Linear Programming
- Guide: Prof. Shivaram Kalyanakrishnan, IIT Bombay

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Three connected pieces of work on sequential decision-making.

- **Regret minimisation** — implemented and benchmarked UCB, KL-UCB and Thompson
  Sampling, including a computationally optimised KL-UCB variant for stochastic
  environments.
- **Planning** — modelled a strategic card game as a Markov Decision Process and
  solved it with Howard's Policy Iteration and Linear Programming to derive
  optimal winning policies.
- **Search** — built a Microchess agent using Minimax with custom heuristics,
  tuning search depth against execution time.