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Found 10 papers, 4 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.
Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees
We also derive a tighter lower bound compared to previous work on the probability of reach-avoidance implied by a RASM, which is required to find a compositional policy with an acceptable probabilistic threshold for complex tasks with multiple edge policies.
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.
Neurosymbolic Reinforcement Learning with Formally Verified Exploration
We present Revel, a partially neural reinforcement learning (RL) framework for provably safe exploration in continuous state and action spaces.
Learning Differentiable Programs with Admissible Neural Heuristics
This relaxed program is differentiable and can be trained end-to-end, and the resulting training loss is an approximately admissible heuristic that can guide the combinatorial search.
Imitation-Projected Programmatic Reinforcement Learning
First, we view our learning task as optimization in policy space, modulo the constraint that the desired policy has a programmatic representation, and solve this optimization problem using a form of mirror descent that takes a gradient step into the unconstrained policy space and then projects back onto the constrained space.
Control Regularization for Reduced Variance Reinforcement Learning
We show that functional regularization yields a bias-variance trade-off, and propose an adaptive tuning strategy to optimize this trade-off.
Finite Automata Can be Linearly Decoded from Language-Recognizing RNNs
We study the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language.
Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks
We investigate the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language.
Programmatically Interpretable Reinforcement Learning
Unlike the popular Deep Reinforcement Learning (DRL) paradigm, which represents policies by neural networks, PIRL represents policies using a high-level, domain-specific programming language.
