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Alessandro Concetti

2 accepted papers

2021

Learning in Non-Cooperative Configurable Markov Decision Processes

NeurIPS 2021poster

The Configurable Markov Decision Process framework includes two entities: a Reinforcement Learning agent and a configurator that can modify some environmental parameters to improve the agent's performance. This presupposes that the two actors have the same reward functions. What if the configurator…

Cited by 13SourcePDFScholar
2021

Provably Efficient Learning of Transferable Rewards

ICML 2021spotlight

The reward function is widely accepted as a succinct, robust, and transferable representation of a task. Typical approaches, at the basis of Inverse Reinforcement Learning (IRL), leverage on expert demonstrations to recover a reward function. In this paper, we study the theoretical properties of the…

Cited by 41SourcePDFScholar