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Found 5 papers, 1 papers with code
Constraint-Generation Policy Optimization (CGPO): Nonlinear Programming for Policy Optimization in Mixed Discrete-Continuous MDPs
To achieve such results, CGPO proposes a bi-level mixed-integer nonlinear optimization framework for optimizing policies within defined expressivity classes (i. e. piecewise (non)-linear) and reduces it to an optimal constraint generation methodology that adversarially generates worst-case state trajectories.
Perimeter Control Using Deep Reinforcement Learning: A Model-free Approach towards Homogeneous Flow Rate Optimization
Moreover, no existing studies have employed reinforcement learning for homogeneous flow rate optimization in microscopic simulation, where spatial characteristics, vehicle-level information, and metering realizations -- often overlooked in macroscopic simulations -- are taken into account.
pyRDDLGym: From RDDL to Gym Environments
We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym environments from RDDL declerative description.
SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems
This method can equally be applied to both the offline, as well as online, variants of the combinatorial problem, in which the problem components (e. g., jobs in scheduling problems) are not known in advance, but rather arrive during the decision-making process.
Learning Control for Air Hockey Striking using Deep Reinforcement Learning
We consider the task of learning control policies for a robotic mechanism striking a puck in an air hockey game.
