Nonparametric Bayesian Inverse Reinforcement Learning for Multiple Reward Functions

By Jaedeug Choi, Kee-Eung Kim. 2012

139 citations recorded by the source corpus.

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Citations in the corpus

The MAGICAL Benchmark for Robust Imitation (2020, neutral): “However, it’s worth noting that unlike, say, HAMDPs [16] or CIRL games [21], IL cannot request clarification from a demonstrator if the supplied demonstrations are ambiguous, which limits its ability to learn the right objective in general.”
A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress (2018, methodology): “dpm-birl is a clustering method that learns multiple specifications using unlabeled fixed-length trajectories [74].”
Convex Optimization: Algorithms and Complexity (2015, unknown): “Finally, Choi and Kim [Choi and Kim, 2012] present a nonparametric Bayesian approach using the Dirichlet process mixture model in order to address the IRL problem with multiple reward functions.”
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning (2018, background): “Some of these approaches include those that aim to incorporate a shared prior over reward functions through extending the Bayesian IRL (Ramachandran & Amir, 2007) framework to the multi-task setting (Dimitrakakis & Rothkopf, 2012; Choi & Kim, 2012).”
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (2019, background): “Alternative approaches to policy similarity fit models to the underlying value function [203] or the unobserved reward function that the skill behavior is implicitly maximizing [309, 214, 261, 64, 18].”
A Function Approximation Method for Model-based High-Dimensional Inverse Reinforcement Learning (2017, methodology): “For motions involving multiple tasks and varying reward functions, methods are developed in [19] and [20], which try to learn multiple reward functions.”
From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following (2019, unknown): “Several previous works consider a multi-task scenario, such as in a Bayesian or meta-learning setting (Li & Burdick, 2017; Dimitrakakis & Rothkopf, 2012; Choi & Kim, 2012).”
Inverse Reinforcement Learning via Function Approximation for Clinical Motion Analysis (2017, methodology): “functions, methods are developed in [17] and [18], which try to learn multiple reward functions.”