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Found 7 papers, 1 papers with code
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.
The Game of Hidden Rules: A New Kind of Benchmark Challenge for Machine Learning
Existing benchmark environments for ML, such as board and video games, offer well-defined benchmarks for progress, but constituent tasks are often complex, and it is frequently unclear how task characteristics contribute to overall difficulty for the machine learner.
Byzantine-Robust Online and Offline Distributed Reinforcement Learning
We consider a distributed reinforcement learning setting where multiple agents separately explore the environment and communicate their experiences through a central server.
Corruption-Robust Offline Reinforcement Learning
Surprisingly, in this case, the knowledge of $\epsilon$ is necessary, as we show that being adaptive to unknown $\epsilon$ is impossible. This again contrasts with recent results on corruption-robust online RL and implies that robust offline RL is a strictly harder problem.
Robust Policy Gradient against Strong Data Corruption
Our first result shows that no algorithm can find a better than $O(\epsilon)$-optimal policy under our attack model.
Error Lower Bounds of Constant Step-size Stochastic Gradient Descent
To our knowledge, this is the first analysis for SGD error lower bound without the strong convexity assumption.
Optimal Attack against Autoregressive Models by Manipulating the Environment
In the white-box setting where the attacker knows the environment and forecast models, we present the optimal attack using LQR for linear models, and Model Predictive Control (MPC) for nonlinear models.
