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Found 7 papers, 3 papers with code
Policy Synthesis and Reinforcement Learning for Discounted LTL
The difficulty of manually specifying reward functions has led to an interest in using linear temporal logic (LTL) to express objectives for reinforcement learning (RL).
Robust Subtask Learning for Compositional Generalization
We formulate the problem as a two agent zero-sum game in which the adversary picks the sequence of subtasks.
Specification-Guided Learning of Nash Equilibria with High Social Welfare
Our empirical evaluation demonstrates that our algorithm computes equilibrium policies with high social welfare, whereas state-of-the-art baselines either fail to compute Nash equilibria or compute ones with comparatively lower social welfare.
Compositional Reinforcement Learning from Logical Specifications
Our approach then incorporates reinforcement learning to learn neural network policies for each edge (sub-task) within a Dijkstra-style planning algorithm to compute a high-level plan in the graph.
Learning Algorithms for Regenerative Stopping Problems with Applications to Shipping Consolidation in Logistics
We study regenerative stopping problems in which the system starts anew whenever the controller decides to stop and the long-term average cost is to be minimized.
Abstract Value Iteration for Hierarchical Reinforcement Learning
We propose a novel hierarchical reinforcement learning framework for control with continuous state and action spaces.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
A Composable Specification Language for Reinforcement Learning Tasks
Reinforcement learning is a promising approach for learning control policies for robot tasks.
