Reinforcement learning in continuous state and action spaces

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Deep learning in neural networks: An overview (2014, neutral): “…Williams, 1996; Prokhorov &Wunsch, 1997; Rummery &Niranjan, 1994; Santamaría, Sutton, & Ram, 1997; Schwartz, 1993; Singh, 1994; Sutton & Barto, 1998; Sutton, Szepesvári, & Maei, 2008; Tsitsiklis & van Roy, 1996; van Hasselt, 2012; Watkins, 1989; Watkins & Dayan, 1992; Wiering & Schmidhuber, 1998b).”
Deep Reinforcement Learning and the Deadly Triad (2018, methodology): “Bootstrapping is also commonly used within policy-gradient and actor-critic methods to learn value functions (Witten, 1977; Barto et al., 1983; Peters and Schaal, 2008; Bhatnagar et al., 2009; van Hasselt, 2012; Degris et al., 2012; Mnih et al., 2016).”
Terrain-adaptive locomotion skills using deep reinforcement learning (2016, background): “…RL to the difficult problem of controlling the movement of physics-based characters has been more limited [Coros et al. 2009; Peng et al. 2015] and has relied on the manual selection of a small set of important features that represent the state of the character and its environment.”
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control (2018, methodology): “In this work, we use the Positive Temporal Difference (PTD) update proposed by (Van Hasselt, 2012) for A π ( s, a ) : where V π ( s ) = E =0 γ t r t | s 0 = s is the value function, which gives the expected discounted cumulative reward from following policy π starting in state s .”