560 citations · 2k across the 22 of their papers we have counts for
22 papers
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction
Dilip Arumugam, Satinder Singh
The Bayes-Adaptive Markov Decision Process (BAMDP) formalism pursues the Bayes-optimal solution to the exploration-exploitation trade-off in reinforcement learning. As the computat…
Markov Decision Processes with Continuous Side Information
Aditya Modi, Nan Jiang, Satinder Singh +1
We consider a reinforcement learning (RL) setting in which the agent interacts with a sequence of episodic MDPs. At the start of each episode the agent has access to some side-info…
Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning
Junhyuk Oh, Satinder Singh, Honglak Lee +1
As a step towards developing zero-shot task generalization capabilities in reinforcement learning (RL), we introduce a new RL problem where the agent should learn to execute sequen…
Repeated Inverse Reinforcement Learning
Kareem Amin, Nan Jiang, Satinder Singh
We introduce a novel repeated Inverse Reinforcement Learning problem: the agent has to act on behalf of a human in a sequence of tasks and wishes to minimize the number of tasks th…
Minimizing Maximum Regret in Commitment Constrained Sequential Decision Making
Qi Zhang, Satinder Singh, Edmund Durfee
In cooperative multiagent planning, it can often be beneficial for an agent to make commitments about aspects of its behavior to others, allowing them in turn to plan their own beh…
Control of Memory, Active Perception, and Action in Minecraft
Junhyuk Oh, Valliappa Chockalingam, Satinder Singh +1
In this paper, we introduce a new set of reinforcement learning (RL) tasks in Minecraft (a flexible 3D world). We then use these tasks to systematically compare and contrast existi…