Work in groups of 2-4 and choose one project. In each project, you will develop three increasingly advanced agents using OpenSpiel, compare their behaviour, and present your results.

Notebook: HCAI_OpenSpiel_Introduction.ipynb (Work in Google Colab or download the .ipynb file to local environment)

Whichever project you choose, you will:

• implement and retain all three agents;
• compare them under consistent conditions;
• include repeated runs where randomness is involved;
• perform at least one controlled modification or ablation;
• analyse at least one failure case;
• use plots and a detailed trajectory to explain behaviour;
• clearly document your modifications;
• demonstrate the project and explain your decisions during the final presentation.

The advanced agent does not need to win every comparison. A correct implementation and a thoughtful analysis of its strengths and limitations are equally important.

• Python: HCAI_OpenSpiel_Python_v1.zip
• C++: HCAI_OpenSpiel_CPP_v0.1.zip (NOTE: converted from Python version to use native OpenSpiel API with the MS Copilot Chat; not fully verified yet)

Topics: Agents · Adversarial Search · Supervised Learning

Develop agents for the classic two-player Connect Four game:

• a one-step agent with a hand-crafted evaluation function;
• a minimax agent with alpha-beta pruning;
• an alpha-beta agent with a learned evaluation function.

You will investigate whether better decisions come from deeper search, better evaluation, or their combination.

Choose this project if you are interested in: competitive games, minimax, heuristic design, and supervised learning.

Topics: Agents · Model-Based Search · Reinforcement Learning

Develop an agent that moves horizontally to catch a descending object:

• a reactive or predictive agent;
• a search agent that plans with the environment model and handles chance outcomes;
• a Q-learning agent trained through interaction.

You will compare explicit planning with learning from experience in a stochastic environment.

Choose this project if you are interested in: dynamic environments, chance nodes, planning, and reinforcement learning.

Topics: Agents · Classical Search · Reinforcement Learning

Develop an agent that must reach a goal without falling from a cliff:

• a greedy agent using immediate successor states;
• an A* agent that plans a safe route;
• a Q-learning agent trained on the same registered OpenSpiel game.

You will examine how different methods balance route length, accumulated cost, and safety.

Choose this project if you are interested in: pathfinding, A*, heuristic design, reward design, and reinforcement learning.