gym-rock-paper-scissors
Rock Paper scissors environment for OpenAI Gym environment
Rock-paper-scissors environment is an implementation of the repeated game of rock-paper-scissors. Where the agents repeatedly play the normal form game of rock paper scissors.
Action space
The action set is common to all agents, and it contains three elements: [ROCK, PAPER, SCISSORS].
State space
The normal form version of rock paper scissors does not have a state representation per se. However we can represent the state of a repeated game by keeping track of the last actions taken by each player for the last n iterations of the game. Let n be an environment parameter, and let (a1t, a2t) be the action pair for both player 1 and 2 at timestep t. The state representation becomes [(a10, a20), (a11, a21), ..., (a1n, a2n)]
Reward function
Follows the classical rules of rock paper scissors. Rock beats scissors, scissors beats paper, paper beats rock. If both players take the same action, they both get get a reward of 0.
Installation
cd gym-rock-paper-scissors
pip install -e .