Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010)

By J. Urgen Schmidhuber. 2010

877 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Cognitive Science & Psychology, Philosophy

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Citations in the corpus

Bridging the Gap Between Value and Policy Based Reinforcement Learning (2017, background): “…extensively studied other exploration strategies including predictive error (Stadie et al., 2015), count based exploration (Belle-mare et al., 2016), information theoretic notions of curiosity (Singh et al., 2004; Schmidhuber, 2010), and under-appreciated reward exploration (Nachum et al., 2017).”
Model-Based Reinforcement Learning for Atari (2019, background): “2017, Chapter 8), capturing important details of the scene (Ha & Schmidhuber, 2018), encouraging exploration (Oh et al., 2015), creating intrinsic motivation (Schmidhuber, 2010) or counterfactual reasoning (Buesing et al., 2019).”
Recurrent World Models Facilitate Policy Evolution (2018, background): “In particular, we can augment the reward function based on improvement in compression quality [77, 80, 81, 83].”
Causally Correct Partial Models for Reinforcement Learning (2020, methodology): “Within MBRL, commonly explored methods include action-conditional, next-step models (Oh et al., 2015; Ha & Schmidhuber, 2018; Chiappa et al., 2017; Schmidhuber, 2010; Xie et al., 2016; Deisenroth & Rasmussen, 2011; Lin & Mitchell, 1992; Li et al., 2015; Diuk et al., 2008; Igl et al., 2018; Ebert et…”
Shaping Belief States with Generative Environment Models for RL (2019, background): “The idea of learning general world models to support decision-making is probably one of the most pervasive ideas in AI research, [30, 11, 41, 42, 14, 43, 44, 45, 46, 33, 30, 47, 48, 3, 49, 50, 51, 52].”
COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration (2019, background): “Curiosity-driven exploration was first proposed in (Schmidhuber, 1990b, 1991) and expanded upon in (Schmidhuber, 2010).”
World Models (2018, methodology): “Large RNNs are highly expressive models that can learn rich spatial and temporal representations of data.”
Curiosity-Driven Exploration by Self-Supervised Prediction (2017, methodology): “Note that ICM-pixels is representative of previous methods which compute information gain by directly using the observation space (Schmidhuber, 2010; Stadie et al., 2015).”
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning (2015, background): “…or Bayesian surprise, which uses the KL divergence to measure the change in an agents internal belief after the observation of new data [8, 22]; measures based on prediction errors of future states such predicted L 1 change, predicted mode change or probability gain [16], or salient event…”
Noisy Networks for Exploration (2017, background): “Many such terms have been proposed, including learning progress [26], compression progress [30], variational information maximisation [14] and prediction gain [4].”