A new benchmark measures lifelong learning in LLM agents with sequential skill-linked tasks, and a group voting method reduces experience replay memory use.
A game theoretic approach to lowering incentives to violate speed limits in Finland
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abstract
We expand on earlier research on the topic by discussing an infinitely repeated game model with a subgame perfect equilibrium strategy profile (SPE) as a solution concept that diminishes incentives to violate speed limits in a carrot and stick fashion. In attempts to construct an SPE strategy profile, the initial state is chosen such that the drivers are playing a mixed strategy whereas the police is not enforcing with certainty. We also postulate a short period version of the repeated game with generalized stage game payoffs. For this game, we construct a multistage strategy profile that is a Nash equilibrium but not an SPE. Some solution candidates are excluded by showing that they do not satisfy a one shot deviation property that is a necessary condition for an SPE profile in a repeated game of perfect information.
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LifelongAgentBench: Evaluating LLM Agents as Lifelong Learners
A new benchmark measures lifelong learning in LLM agents with sequential skill-linked tasks, and a group voting method reduces experience replay memory use.