Free-energy-based reinforcement learning in a partially observable environment

By Makoto Otsuka, Junichiro Yoshimoto, Kenji Doya. 2010

18 citations recorded by the source corpus.

Subfields: Reinforcement Learning, Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Reinforcement Learning

1. J. Urgen Schmidhuber
2. Sergey Levine
3. David R Ha
4. Chelsea Finn
5. Pieter Abbeel
6. Andrew Barto
7. Yoshua Bengio
8. Shimon Whiteson

In Probabilistic & Bayesian Methods

1. J. Urgen Schmidhuber

Citations in the corpus

• Deep learning in neural networks: An overview (2014, background): “However, memories of previous events can help to deal with partially observable Markov decision problems (POMDPs) (e.g., Schmidhuber, 1990d, 1991c; Ring, 1991, 1993, 1994; Williams, 1992a; Lin, 1993; Teller, 1994; Kaelbling et al., 1995; Littman et al., 1995; Boutilier and Poole, 1996; Jaakkola et a”
• On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, background): “This triggered work on partially observable Markov decision problems (POMDPs) [223, 222, 227, 204, 205, 206, 316, 148, 278, 122, 152, 25, 114, 160, 126, 308, 309, 183].”