Simulation demonstrates reward-maximal policy learning under linear temporal logic constraints in deep reinforcement learning, indicating effective guidance via cycle experience replay.
Key Points
A single optimization formulation enables deep reinforcement learning agents to maximize scalar rewards while strictly satisfying linear temporal logic constraints.
Cycle experience replay mitigates objective sparsity by automatically directing policy exploration toward satisfying specified linear temporal logic properties.
Simulation benchmarks in continuous and discrete domains confirm successful synthesis of performant deep policies, highlighting broader reinforcement learning applicability.