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Found 6 papers, 2 papers with code
Deep Policy Optimization with Temporal Logic Constraints
In our work, we consider the setting where the task is specified by an LTL objective and there is an additional scalar reward that we need to optimize.
Eventual Discounting Temporal Logic Counterfactual Experience Replay
Linear temporal logic (LTL) offers a simplified way of specifying tasks for policy optimization that may otherwise be difficult to describe with scalar reward functions.
Policy Optimization with Linear Temporal Logic Constraints
We study the problem of policy optimization (PO) with linear temporal logic (LTL) constraints.
Minimax Model Learning
We present a novel off-policy loss function for learning a transition model in model-based reinforcement learning.
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning
We offer an experimental benchmark and empirical study for off-policy policy evaluation (OPE) in reinforcement learning, which is a key problem in many safety critical applications.
Batch Policy Learning under Constraints
When learning policies for real-world domains, two important questions arise: (i) how to efficiently use pre-collected off-policy, non-optimal behavior data; and (ii) how to mediate among different competing objectives and constraints.
