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Markov Decision Processes with Time-Varying Geometric Discounting
We take a game-theoretic perspective -- whereby each time step is treated as an independent decision maker with their own (fixed) discount factor -- and we study the subgame perfect equilibrium (SPE) of the resulting game as well as the related algorithmic problems.
Stochastic Principal-Agent Problems: Efficient Computation and Learning
Both players are far-sighted, aiming to maximize their total payoffs over the time horizon.
Learning to Manipulate a Commitment Optimizer
We consider the scenario where the follower is not given any information about the leader's payoffs to begin with but has to learn to manipulate by interacting with the leader.
Online Reinforcement Learning with Uncertain Episode Lengths
We show that minimizing regret with this new general discounting is equivalent to minimizing regret with uncertain episode lengths.
Socially Fair Reinforcement Learning
We consider the problem of minimizing regret with respect to the fair policies maximizing three different fair objectives -- minimum welfare, generalized Gini welfare, and Nash social welfare.
Admissible Policy Teaching through Reward Design
This problem can be viewed as a dual to the problem of optimal reward poisoning attacks: instead of forcing an agent to adopt a specific policy, the reward designer incentivizes an agent to avoid taking actions that are inadmissible in certain states.
Your College Dorm and Dormmates: Fair Resource Sharing with Externalities
Clearly, the strong notion of envy-freeness, where no agent envies another for their resource or mates, cannot always be achieved and we show that even deciding the existence of such a strongly envy-free assignment is an intractable problem.
Computer Science and Game Theory
Budget-feasible Maximum Nash Social Welfare Allocation is Almost Envy-free
The Nash social welfare (NSW) is a well-known social welfare measurement that balances individual utilities and the overall efficiency.
Fairness
Computer Science and Game Theory
Multiagent Systems
Optimally Deceiving a Learning Leader in Stackelberg Games
For this strategic behavior to be successful, the main challenge faced by the follower is to pinpoint the payoffs that would make the learning algorithm compute a commitment so that best responding to it maximizes the follower's utility, according to his true payoffs.
Manipulating a Learning Defender and Ways to Counteract
We then apply a game-theoretic framework at a higher level to counteract such manipulation, in which the defender commits to a policy that specifies her strategy commitment according to the learned information.
