Coin

JaxMARL contains an implementation of the Coin Game environment presented in Model-Free Opponent Shaping (Lu et al.). The original description and usage of the environment is from Maintaining cooperation in complex social dilemmas using deep reinforcement learning (Lerer et al.), and Learning with Opponent-Learning Awareness (Foerster et al.) is the first to popularize its use for opponent shaping. A description from Model-Free Opponent Shaping:

The Coin Game is a multi-agent grid-world environment that simulates social dilemmas like the IPD but with high dimensional dynamic states. First proposed by Lerer & Peysakhovich (2017), the game consists of two players, labeled red and blue respectively, who are tasked with picking up coins, also labeled red and blue respectively, in a 3x3 grid. If a player picks up any coin by moving into the same position as the coin, they receive a reward of +1.  However, if they pick up a coin of the other player’s color, the other player receives a reward of −2. Thus, if both agents play greedily and pick up every coin, the expected reward for both agents is 0.