APES: a Python toolbox for simulating reinforcement learning environments

TL;DR

APES is a Python toolkit that simplifies the creation of customizable 2D grid-world environments for reinforcement learning, supporting multiple agents, vision simulation, and flexible item placement.

Contribution

It introduces a highly customizable, open-source Python package for designing complex 2D reinforcement learning environments with advanced features.

Findings

Enables simulation of diverse vision fields for agents

Supports multi-agent interactions in custom environments

Facilitates flexible environment and reward design

Abstract

Assisted by neural networks, reinforcement learning agents have been able to solve increasingly complex tasks over the last years. The simulation environment in which the agents interact is an essential component in any reinforcement learning problem. The environment simulates the dynamics of the agents' world and hence provides feedback to their actions in terms of state observations and external rewards. To ease the design and simulation of such environments this work introduces $\texttt{APES}$, a highly customizable and open source package in Python to create 2D grid-world environments for reinforcement learning problems. $\texttt{APES}$ equips agents with algorithms to simulate any field of vision, it allows the creation and positioning of items and rewards according to user-defined rules, and supports the interaction of multiple agents.