Reinforcement Learning
Learning Regularized Monotone Graphon Mean-Field Games
In contrast, we design a discrete-time algorithm and derive its convergence rate solely under weakly monotone conditions.
Efficient Diffusion Policies for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to learn optimal policies from offline datasets, where the parameterization of policies is crucial but often overlooked.
Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage
Typically, deep RL algorithms learn a policy in an online trial-and-error fashion using millions to billions of data.
Efficient Model-Free Exploration in Low-Rank MDPs
What are the right computational primitives for exploration?
Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees Dohyeong Kim
However, the nonlinearity of risk measures makes it challenging to achieve convergence and optimality.
QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers
Queuing network control determines the allocation of scarce resources to manage congestion, a fundamental problem in manufacturing, communications, and healthcare.
Multi-Agent Meta-Reinforcement Learning: Sharper Convergence Rates with Task Similarity
We further provide numerical simulations to corroborate our theoretical findings.