Search Results for author: Sanmit Narvekar

Found 5 papers, 3 papers with code

Generalizing Curricula for Reinforcement Learning

no code implementations ICML Workshop LifelongML 2020 Sanmit Narvekar, Peter Stone

However, there is structure that can be exploited between tasks and agents, such that knowledge gained developing a curriculum for one task can be reused to speed up creating a curriculum for a new task.

reinforcement-learning Reinforcement Learning (RL)

Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey

no code implementations10 Mar 2020 Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, Peter Stone

Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback.

reinforcement-learning Reinforcement Learning (RL) +1

RecSim: A Configurable Simulation Platform for Recommender Systems

1 code implementation11 Sep 2019 Eugene Ie, Chih-Wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, Craig Boutilier

We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users.

Recommendation Systems reinforcement-learning +1

Learning Curriculum Policies for Reinforcement Learning

1 code implementation1 Dec 2018 Sanmit Narvekar, Peter Stone

Curriculum learning in reinforcement learning is a training methodology that seeks to speed up learning of a difficult target task, by first training on a series of simpler tasks and transferring the knowledge acquired to the target task.

reinforcement-learning Reinforcement Learning (RL) +1

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