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Found 7 papers, 2 papers with code
Optimal Attack and Defense for Reinforcement Learning
Although the defense problem is NP-hard, we show that optimal Markovian defenses can be computed (learned) in polynomial time (sample complexity) in many scenarios.
Anytime-Constrained Reinforcement Learning
Our reduction yields planning and learning algorithms that are time and sample-efficient for tabular cMDPs so long as the precision of the costs is logarithmic in the size of the cMDP.
Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value
We study the game modification problem, where a benevolent game designer or a malevolent adversary modifies the reward function of a zero-sum Markov game so that a target deterministic or stochastic policy profile becomes the unique Markov perfect Nash equilibrium and has a value within a target range, in a way that minimizes the modification cost.
VISER: A Tractable Solution Concept for Games with Information Asymmetry
Many real-world games suffer from information asymmetry: one player is only aware of their own payoffs while the other player has the full game information.
On Faking a Nash Equilibrium
We characterize offline data poisoning attacks on Multi-Agent Reinforcement Learning (MARL), where an attacker may change a data set in an attempt to install a (potentially fictitious) unique Markov-perfect Nash equilibrium.
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning
In offline multi-agent reinforcement learning (MARL), agents estimate policies from a given dataset.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Approximating Pandora's Box with Correlations
For distributions of support $m$, UDT admits a $\log m$ approximation, and while a constant factor approximation in polynomial time is a long-standing open problem, constant factor approximations are achievable in subexponential time (arXiv:1906. 11385).
