Sample-Efficient Deep RL with Generative Adversarial Tree Search

By Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Emma Brunskill, Zachary Chase Lipton, Anima Anandkumar. 2018

17 citations recorded by the source corpus.

Subfields: Reinforcement Learning

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. Pieter Abbeel
3. Igor Mordatch
4. Chelsea Finn
5. Aravind Rajeswaran
6. Vikash Kumar
7. Ofir Nachum
8. Shixiang Shane Gu

Citations in the corpus

• Model-Based Reinforcement Learning for Atari (2019, methodology): “Azizzadenesheli et al. (2018) primarily discuss various failure modes of the GATS algorithm.”
• Adaptive Online Planning for Continual Lifelong Learning (2019, methodology): “[2, 24] learn dynamics models and perform MPC using them.”
• Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control (2018, background): “[56] consider learning learning an internal model for discrete action domains like ALE and use short horizon MCTS for planning.”
• Combating the Compounding-Error Problem with a Multi-step Model (2019, methodology): “, 2018], hallucinating training examples [Talvitie, 2014; Azizzadenesheli et al., 2018; Venkatraman et al., 2015; Oh et al., 2015; Bengio et al., 2015], using ensembles [Kurutach et al.”
• Do recent advancements in model-based deep reinforcement learning really improve data efficiency? (2019, background): “Azizzadenesheli et al. (2018) proposed Generative Adversarial Tree Search (GATS).”