2 citations · 2 across the 8 of their papers we have counts for
4 papers · 1 filter
Efficient Exploration Is Enough
Mikel Malagón, Jon Vadillo, Josu Ceberio +2
This work introduces an alternative view of efficient exploration and studies its theoretical and empirical implications in the absence of extrinsic rewards. Specifically, we defin…
Model-Based Exploration in Monitored Markov Decision Processes
Alireza Kazemipour, Simone Parisi, Matthew E. Taylor +1
A tenet of reinforcement learning is that the agent always observes rewards. However, this is not true in many realistic settings, e.g., a human observer may not always be availabl…
Beyond Optimism: Exploration With Partially Observable Rewards
Simone Parisi, Alireza Kazemipour, Michael Bowling
Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent le…
Monitored Markov Decision Processes
Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour +2
In reinforcement learning (RL), an agent learns to perform a task by interacting with an environment and receiving feedback (a numerical reward) for its actions. However, the assum…