RLPy: a value-function-based reinforcement learning framework for education and research

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OpenAI Gym (2016, background): “…released, such as the Arcade Learning Environment (ALE) [5], which exposed a collection of Atari 2600 games as reinforcement learning problems, and recently the RLLab benchmark for continuous control [6], to which we refer the reader for a survey on other RL benchmarks, including [7, 8, 9, 10, 11].”
Generative Adversarial Imitation Learning (2016, methodology): “We evaluated GAIL against baselines on 9 physics-based control tasks, ranging from low-dimensional control tasks from the classic RL literature—the cartpole [2], acrobot [7], and mountain car [15]—to difficult high-dimensional tasks such as a 3D humanoid locomotion, solved only recently by modelfree”
Programmatically Interpretable Reinforcement Learning (2018, methodology): “This implementation is based on the system presented in (Geramifard et al., 2015).”
Dopamine: A Research Framework for Deep Reinforcement Learning (2018, methodology): “…2016) provides object-oriented support; PyBrain (Schaul et al., 2010) supports neural networks, and is perhaps closest to more recent frameworks; RLPy (Geramifard et al., 2015) is a lighter framework written in Python, with similar goals to RLGlue and also emphasizing instructional purposes.”
An Introduction to Deep Reinforcement Learning (2018, unknown): “• RLPy (Geramifard et al., 2015) is a framework focused on valuebased RL using linear function approximators with discrete actions.”