Direct Policy Iteration with Demonstrations

By Jessica J. Chemali, Alessandro Lazaric. 2015

39 citations recorded by the source corpus.

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In Reinforcement Learning

Citations in the corpus

Observe and Look Further: Achieving Consistent Performance on Atari (2018, methodology): “More recent approaches have explicitly combined expert demonstrations with RL data during the learning of the policy or action-value function [3, 11, 15].”
Off-Policy Deep Reinforcement Learning without Exploration (2018, background): “In recent years, combining imitation with reinforcement learning, via learning from demonstrations (Kim et al., 2013; Piot et al., 2014; Chemali & Lazaric, 2015) has grown in popularity with extensions to deep reinforcement learning (Hester et al., 2017; Veˇcer´ık et al., 2017).”
Deep Q-learning From Demonstrations (2017, methodology): “This framework was appropriately called Reinforcement Learning with Expert Demonstrations (RLED) in (Piot, Geist, and Pietquin 2014a) and is also evaluated in (Kim et al. 2013; Chemali and Lezaric 2015).”
Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling (2019, methodology): “Demonstrations have been widely used to improve the efficiency of RL [Kim et al., 2013, Chemali and Lazaric, 2015, Piot et al., 2014], and a common paradigm for continuous state and action space is to initialize with RL algorithms with a good policy or Q function [Rajeswaran et al., 2017, Nair et…”
Reinforcement Learning from Imperfect Demonstrations (2018, methodology): “We follow the Reinforcement Learning with Expert Demonstrations (RLED) framework (Chemali & Lazaric, 2015; Kim et al., 2013; Piot et al., 2014), where both rewards and actions are available in the demonstrations.”
Learning from Demonstrations for Real World Reinforcement Learning (2017, methodology): “This framework was appropriately called Reinforcement Learning with Expert Demonstrations (RLED) in Piot et al. (2014a) and is also evaluated in Kim et al. (2013); Chemali & Lezaric (2015).”
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning (2019, background): “…problem (Schaal, 1999; Argall et al., 2009; Hussein et al., 2017) and also continues to be a highly active area of research (Kim et al., 2013; Piot et al., 2014; Chemali & Lazaric, 2015; Hester et al., 2018; Ho et al., 2016; Sun et al., 2017; 2018; Cheng et al., 2018; Gao et al., 2018).”
Transfer Learning in Deep Reinforcement Learning: A Survey (2020, background): “Later, [94] introduced the Direct Policy Iteration with Demonstrations (DPID) algorithm.”