Bayesian Learning of Recursively Factored Environments

By Marc G. Bellemare, Joel Veness, Michael H. Bowling. 2013

29 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. Sergey Levine
2. Marc G. Bellemare
3. Pieter Abbeel
4. Joelle Pineau
5. Yuxi Li
6. Marlos C. Machado
7. Michael H. Bowling
8. Honglak Lee

In Probabilistic & Bayesian Methods

1. Erik Talvitie
2. Marc G. Bellemare
3. Joel Veness
4. Honglak Lee
5. Satinder Singh
6. Joelle Pineau
7. Michael H. Bowling
8. Yoshua Bengio

Citations in the corpus

• Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents (2017, methodology): “(Bellemare et al. 2013; Mnih et al. 2013) used ✏-greedy action selection with ✏ = .05.”
• Action-Conditional Video Prediction using Deep Networks in Atari Games (2015, background): “One line of work [3, 4] divides game images into patches and applies a Bayesian framework to predict patch-based observations.”
• Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games (2016, methodology): “The Arcade Learning Environment (ALE) includes an ATARI 2600 emulator and about 50 games [Bellemare et al. , 2013b].”
• Skip Context Tree Switching (2014, methodology): “We used the Arcade Learning Environment (Bellemare et al., 2013a), an interface that allows agents to interact with Atari 2600 games.”
• The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach (2018, methodology): “…MDP (for example, by factorizing the state transition model) has also been investigated extensively in the literature (Boutilier et al., 1999; Degris et al., 2006; Strehl et al., 2007; Ross & Pineau, 2008; Bellemare et al., 2013; Wu et al., 2014; Osband & Van Roy, 2014; Hallak et al., 2015).”