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.
A safe navigation algorithm for differential-drive mobile robots by using fuzzy logic reward function-based deep reinforcement learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.