Given the plethora of Reinforcement Learning algorithms available in the literature, it can prove challenging to decide on the most appropriate one to use in order to solve a given Reinforcement Learning task. This work presents a benchmark study on the performance of several Reinforcement Learning algorithms for discrete learning environments. The study includes several deep as well as non-deep learning algorithms, with special focus on the Deep Q-Network algorithm and its variants. Neural Fitted Q-Iteration, the predecessor of Deep Q-Network as well as Vanilla Policy Gradient and a planner were also included in this assessment in order to provide a wider range of comparison between different approaches and paradigms. Three learning environments were used in order to carry out the tests, including a 2D maze and two OpenAI Gym environments, namely a custom-built Foraging/Tagging environment and the CartPole environment.
CITATION STYLE
Duarte, F. F., Lau, N., Pereira, A., & Reis, L. P. (2020). Benchmarking Deep and Non-deep Reinforcement Learning Algorithms for Discrete Environments. In Advances in Intelligent Systems and Computing (Vol. 1093 AISC, pp. 263–275). Springer. https://doi.org/10.1007/978-3-030-36150-1_22
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