Iterated Prisoner's Dilemma

Prisoner's dilemma (PD) and its iterated variant (IPD) is a well-known problem in game theory. The goal of this project is to create and compare optimization algorithms that find a good player for a particular IPD problem instance.

An IPD problem instance is given by

• the payoff matrix and
• the number of iterations.

The payoff matrix must be set up such that the game is fair for both players. For 4 positive numbers a, b, c, and d, the matrix has the following form:

where in each pair (r1, r2) denotes the rewards for player 1 and player 2, respectively.

Depending on relations among A, B, C, and D, the payoff matrix may be set up in such a way that

• there is a proper dilemma,
• no dilemma is present,
• there is no dominant strategy,
• the best strategy require the decisions of the players to alternate,
• etc.

You should choose several interesting PM setups and try to find players for them.

There are many possible representations of the players/strategies:

• finite state machine (that can be encoded in a binary string),
• set of rules,
• decision tree,
• neural network,
• …

The chosen representation shall be consulted with the teacher.

IPD is a two-player game. To evaluate the quality of a player is thus possible only by playing games against other players, i.e., the quality of a player depends on the set of chosen opponents. The choice of this reference set is up to you, it may be

• a static set of predefined players,
• the other players in population,
• a combination of the above options, …

The goal is to maximize the sum of rewards across all played IPD games.

In no particular order:

• What is the strategy used by the best player for individual PM setups?
• How does the initialization affect the resulting best player? What if we seed the population with a specified number of copies of known strategies?
• Is it possible to evolve a player, that plays well for any setup of PM?
• Is it possible to evolve a player, that plays well against any opponent?

Take these questions as an inspiration of what you can study in your project.