A bi-level game-theoretic optimal control plus reinforcement learning framework enables competitor-aware energy management and pit-stop scheduling that exploits aerodynamic drafting in simulated electric endurance races.
Policy invariance under reward transformations: Theory and application to reward shaping,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
PBRS-augmented RL trained in simple settings transfers zero-shot to complex UAV environments when wrapped with a CLF-CBF-QP safety filter, yielding shorter missions and formal safety guarantees.
Fuzzy logic-based adaptive reward shaping improves RL convergence speed, reduces variability, and boosts success rates by up to 5% in drone racing simulations compared to standard rewards.
citing papers explorer
-
Competitor-aware Race Management for Electric Endurance Racing
A bi-level game-theoretic optimal control plus reinforcement learning framework enables competitor-aware energy management and pit-stop scheduling that exploits aerodynamic drafting in simulated electric endurance races.
-
Zero-Shot, Safe and Time-Efficient UAV Navigation via Potential-Based Reward Shaping, Control Lyapunov and Barrier Functions
PBRS-augmented RL trained in simple settings transfers zero-shot to complex UAV environments when wrapped with a CLF-CBF-QP safety filter, yielding shorter missions and formal safety guarantees.
-
Fuzzy Logic Theory-based Adaptive Reward Shaping for Robust Reinforcement Learning (FARS)
Fuzzy logic-based adaptive reward shaping improves RL convergence speed, reduces variability, and boosts success rates by up to 5% in drone racing simulations compared to standard rewards.