Differentiable relaxation of LTL automata via soft labeling enables gradient-based RL from formal specifications, with theoretical bounds on discrete-differentiable discrepancy and up to 2x returns on nonlinear tasks.
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UNVERDICTED 4representative citing papers
Secure planning against stealthy attacks on robot actuators in unknown stochastic environments is achieved by modeling the problem as a stochastic game and solving it with model-free RL to satisfy a combined LTL formula.
A model-free RL methodology is developed to maximize the probability of LTL satisfaction in unknown stochastic games when the derived DRA has a single Rabin pair, with a generalization providing lower bounds for multiple pairs.
A model-free RL algorithm uses LTL-derived rewards and path-dependent discounts to learn policies that maximize LTL satisfaction probability with convergence guarantees.
citing papers explorer
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Accelerated Learning with Linear Temporal Logic using Differentiable Simulation
Differentiable relaxation of LTL automata via soft labeling enables gradient-based RL from formal specifications, with theoretical bounds on discrete-differentiable discrepancy and up to 2x returns on nonlinear tasks.
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Secure Planning Against Stealthy Attacks via Model-Free Reinforcement Learning
Secure planning against stealthy attacks on robot actuators in unknown stochastic environments is achieved by modeling the problem as a stochastic game and solving it with model-free RL to satisfy a combined LTL formula.
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Model-Free Reinforcement Learning for Stochastic Games with Linear Temporal Logic Objectives
A model-free RL methodology is developed to maximize the probability of LTL satisfaction in unknown stochastic games when the derived DRA has a single Rabin pair, with a generalization providing lower bounds for multiple pairs.
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Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning
A model-free RL algorithm uses LTL-derived rewards and path-dependent discounts to learn policies that maximize LTL satisfaction probability with convergence guarantees.