Fast Online Q(λ)

By Marco A. Wiering, J. Urgen Schmidhuber. 1998

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “…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).”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “The parameters or weights of C are trained to maximize reward by a standard RL method such as Q-learning or similar methods [17, 292, 293, 172, 254, 212, 262, 10, 122, 188, 157, 281, 26, 216, 197, 272, 311, 9, 163, 174, 22, 27, 2, 137, 276, 156, 284].”
Exploiting Best-Match Equations for Efficient Reinforcement Learning (2011, background): “Wiering and Schmidhuber (1998) showed that by postponing updates a computationally efficient version of Q(λ) can be constructed that does not rely on placing a bound on the trace length.”
Learning reliable and efficient navigation with a humanoid (2010, methodology): “In particular, we apply Sarsa(λ) RL [2] to determine the optimal action for each state, i.”
Nash Q-Learning for General-Sum Stochastic Games (2003, background): “Q(λ) learning (Peng and Williams, 1996, Wiering and Schmidhuber, 1998) promises an interesting way to speed up the learning process.”
Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) (2010, unknown): “Others postpone updates until needed [119].”
Nash Q-Learning for General-Sum Stochastic Games (2003, background): “Q(λ) learning (Peng and Williams, 1996, Wiering and Schmidhuber, 1998) promises an interesting way to speed up the learning process.”