Generalization and Exploitation: Meta-GSAC for Multi-Task UAV Path Planning and Obstacle Avoidance

Deep reinforcement learning (DRL) is extensively applied in autonomous unmanned aerial vehicle (UAV) control yet faces critical challenges regarding adaptability and generalization in dynamic environments. To address these limitations, this paper proposes the Meta Gated Transformer-XL Soft Actor-Critic (Meta-GSAC) algorithm. This framework integrates a Gated Transformer-XL module to capture long-term temporal dependencies from multimodal inputs and incorporates the Reptile algorithm to facilitate multi-task meta-learning. Experimental results demonstrate that Meta-GSAC significantly outperforms standard baselines. Notably, it achieves optimal policy convergence with approximately 50% fewer training epochs while effectively eliminating the high-frequency control oscillations observed in the GSAC baseline. Moreover, the proposed method exhibits superior few-shot adaptation capabilities, enabling the UAV to rapidly adapt to novel task scenarios with minimal gradient updates.