3 citations · 8 across the 4 of their papers we have counts for
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cs.AI2022★ 2 cited
Reward is not Necessary: How to Create a Modular & Compositional Self-Preserving Agent for Life-Long Learning
Thomas J. Ringstrom
Reinforcement Learning views the maximization of rewards and avoidance of punishments as central to explaining goal-directed behavior. However, over a life, organisms will need to…
cs.AI2020★ 3 cited
Goal Kernel Planning: Linearly-Solvable Non-Markovian Policies for Logical Tasks with Goal-Conditioned Options
Thomas J. Ringstrom, Mohammadhosein Hasanbeig, Alessandro Abate
In the domain of hierarchical planning, compositionality, abstraction, and task transfer are crucial for designing algorithms that can efficiently solve a variety of problems with…
cs.AI2019★ 3 cited
Constraint Satisfaction Propagation: Non-stationary Policy Synthesis for Temporal Logic Planning
Thomas J. Ringstrom, Paul R. Schrater
Problems arise when using reward functions to capture dependencies between sequential time-constrained goal states because the state-space must be prohibitively expanded to accommo…