Zero-sum Markov games arise in a wide variety of sequential decision-making problems such as adversarial learning and planning against modeled uncertainties. However, prior work on finite-sample guarantees on the learned state-action value function ($Q$-function) for zero-sum Markov games is largely restricted to finit...
Kushagra Gupta, Jing-Qi Li, Caden Armstrong et al.· 0 citations
The chance-constrained correlated equilibrium concept provides a robust coordination technique for self-interested agents whose costs are uncertain to a central coordinator, but the number of joint actions considered in its computation grows exponentially with the number of agents. We propose an exact-one resource-assi...
Jaehan Im, U. Topcu, David Fridovich-Keil· 0 citations
Policy gradient methods for on-policy reinforcement learning (RL) can become unstable when expensive, scarce target-domain data yield noisy gradient estimates. We address this challenge by complementing limited high-fidelity (HF) target-domain data with abundant, cheap, but biased low-fidelity (LF) data, e.g., from a s...
Xin-Jie Liu, Rui-Han Zhao, A. Chaudhuri et al.· 0 citations
We consider the problem of objective inference in the context of receding-horizon linear-quadratic regulator (LQR). In this setting, we are given sequential state-action observations, where each observed action is the first control of a newly solved finite-horizon LQR problem. We characterize when the objective of that...
Zhi-Yuan Jin, Jing-Qi Li, David Fridovich-Keil· 0 citations
CLAW (Context-conditioned Low-rank Adaptation of World models), which addresses the problem of adapting a world model to an unknown test-time environment, drawn from a known environment family, using only a few episodes of interaction by using a hypernetwork to generate low-rank adapters at test time.
Fernando Palafox, David Fridovich-Keil· 0 citations
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