This code is for a simple 2D game environment that can be used in developing reinforcement learning models. It is designed to be compact but flexible, enabling the implementation of diverse set of games. Furthermore, it offers precise tuning of the game difficulty, facilitating the construction of curricula to aid training. The code is in Lua+Torch, and it offers rapid prototyping of games and is easy to connect to models that control the agent’s behavior. For more details, see our paper. The environment is presented to the agent as a list of sentences, each describing an item in the game. For example, an agent might see “Block at [-1,4]. Switch at [+3,0] with blue color. Info: change switch to red.” However, note that we use egocentric spatial coordinates, meaning that the environment updates the locations of each object after an action. The environments are generated randomly with some distribution on the various items. For example, we usually specify a uniform distribution over height and width, and a percentage of wall blocks and water blocks.