APES: a Python toolbox for simulating reinforcement learning environments. 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 APES, a highly customizable and open source package in Python to create 2D grid-world environments for reinforcement learning problems. 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.
Keywords for this software
References in zbMATH (referenced in 2 articles , 1 standard article )
Showing results 1 to 2 of 2.
- Wang, Kevin YX; Tarr, Garth; Yang, Jean Yh; Mueller, Samuel: Fast and approximate exhaustive variable selection for generalised linear models with APES (2019)
- Aqeel Labash; Ardi Tampuu; Tambet Matiisen; Jaan Aru; Raul Vicente: APES: a Python toolbox for simulating reinforcement learning environments (2018) arXiv