4 papers
Provably Safe Sim-to-Real Transfer
Tingting Ni, Maryam Kamgarpour
To mitigate the sample complexity of real-world reinforcement learning (RL), a common practice is to first train a policy in a simulator, where samples are cheap, and then deploy t…
Constrained Meta Reinforcement Learning with Provable Test-Time Safety
Tingting Ni, Maryam Kamgarpour
Meta reinforcement learning (RL) allows agents to leverage experience across a distribution of tasks on which the agent can train at will, enabling faster learning of optimal polic…
On the characterization of constrained correlated equilibria in Markov games
Tingting Ni, Anna Maddux, Maryam Kamgarpour
Markov games with coupling constraints model constrained dynamical decision-making involving self-interested agents, where the feasibility of an individual agent's strategy depends…
A learning-based approach to stochastic optimal control under reach-avoid constraint
Tingting Ni, Maryam Kamgarpour
We develop a model-free approach to optimally control stochastic, Markovian systems subject to a reach-avoid constraint. Specifically, the state trajectory must remain within a saf…