Model-Based Planning with Discrete and Continuous Actions

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Learning Latent Dynamics for Planning from Pixels (2018, methodology): “Related Work Previous work in model-based reinforcement learning has focused on planning in low-dimensional state spaces (Gal et al., 2016; Higuera et al., 2018; Henaff et al., 2018; Chua et al., 2018), combining the benefits of model-based and model-free approaches (Kalweit & Boedecker, 2017; Nagab”
Learning and Querying Fast Generative Models for Reinforcement Learning (2018, methodology): “Therefore, we take a heurisitic approach similar to Henaff et al. (2017) (and related to Bengio et al., 2013): Instead of feeding the sampled one-hot environment action to the model, we can instead directly feed the probability vector π r ( a t (cid:48) | s t (cid:48) | t ) into the environment…”
Dream to Control: Learning Behaviors by Latent Imagination (2019, background): “Planning with neural network gradients was shown on small problems (Schmidhuber, 1990; Henaff et al., 2018) but has been challenging to scale (Parmas et al., 2019).”
Mastering Atari with Discrete World Models (2020, background): “World models Several model-based agents focus on proprioceptive inputs (Watter et al., 2015; Gal et al., 2016; Higuera et al., 2018; Henaff et al., 2018; Chua et al., 2018; Wang et al., 2019; Wang and Ba, 2019), model images without using them for planning (Oh et al., 2015; Krishnan et al., 2015;…”