Tic Tac Toe

A Tic Tac Toe implementation where two reinforcement-learning agents (temporal-difference / value-based learning) train against each other, and a human can play against a trained agent from the command line.

How it works

• state.py — board representation (3x3 numpy array of 'X', 'O', '-') and helpers to detect potential moves and winning lines.
• a_player.py — APlayer base class shared by all player types (move, win-check, game lifecycle hooks).
• machine_player.py — MachinePlayer, a TD-learning agent. It keeps a value table (state string -> expected reward) in a dict, picks the move leading to the best-valued next state, occasionally explores a random move (p_explore), and updates values towards the outcome of each game using a learning rate alpha.
• human_player.py — HumanPlayer, lets a human enter moves via the console (German prompts).
• player_provider.py — PlayerProvider pairs two players for a game and randomly decides who starts.
• helper.py — small numeric helpers (softmax/sampling), not currently used by the main game loop.
• tic_tac_toe.py — entry point: runs training and/or play loops.
• values_x.json / values_o.json — persisted value tables for the X and O machine players, loaded/saved across runs so training progress carries over.

Requirements

• Python 3.12+
• numpy (see .venv for an existing virtual environment, or install with pip install numpy)

Usage

Run from the project root:

python tic_tac_toe.py [options]

Options:

Flag	Default	Description
--verbose	False	Print detailed per-move debug output
--train	False	Train the machine players before playing
--num	5	Number of training iterations / batches
--human	False	Let a human play as X against the machine player as O
--load	True	Load existing value tables from values_x.json/values_o.json

Examples:

# Train the two machine players against each other, saving progress
python tic_tac_toe.py --train --num 50

# Play as a human (X) against the trained machine player (O)
python tic_tac_toe.py --human

# Watch two machine players play a few verbose games
python tic_tac_toe.py --verbose

During training, values_x.json and values_o.json are updated after each batch of games, so training can be interrupted and resumed.