Adversarial Reinforcement Learning for Robust Planetary Landing

Reinforcement learning (RL) policies trained in simulation often degrade once deployed on real systems, where the controller must reject external disturbances that were never encountered in simulation. Robust RL addresses this by exposing the controller to perturbations while it learns, through domain randomization, adversarial minimax formulations, or probabilistic mixtures of protagonist and adversarial behavior. However, an unregulated disturbance mechanism destabilizes training and often collapses nominal performance relative to standard, non-robust methods. We propose cost-to-disturbance ratio adversarial training (CoDRA), a framework that expresses the controller--adversary trade-off as a ratio of accumulated cost to accumulated squared disturbance norm, and optimizes it through a self-normalized actor--critic update. In this algorithm, each value term is scaled by a stop-gradient normalization constant computed from the current batch. This moderates the adversary's incentive without altering the controller's own update, and requires neither an explicit disturbance penalty nor an auxiliary trade-off parameter. We evaluate CoDRA on two MuJoCo pendulum environments under force and mass sweeps. On InvertedDoublePendulum, CoDRA attains the lowest cost at every force level, including a force outside the range seen during training, and in all but one cell of the mass grid, whereas its advantage is less pronounced on the milder InvertedPendulum.

Reinforcement learning (RL) achieves strong performance in sequential decision-making but remains brittle under dynamic uncertainty and distributional shifts. Robust Adversarial Reinforcement Learning (RARL) improves robustness via worst-case perturbations, but existing approaches frequently suffer from unstable optimization and degraded value estimation. In particular, overly aggressive adversaries can drive the agent toward uninformative failure states, while adversarial perturbations amplify disagreement between double critics and introduce biased value targets. We propose a unified framework, RACER (Risk-sensitive robust Adversarial critic ConsistEncy-regularized Reinforcement learning), that revisits adversarial RL from a risk-sensitive perspective. First, we introduce a state-dependent adversarial objective that adaptively regulates perturbation strength, suppressing harmful disturbances while preserving informative exploration. Second, we propose critic consistency regularization to reduce disagreement between Q-value estimators and stabilize learning. Comprehensive experiments on challenging continuous control benchmarks demonstrate that RACER consistently improves performance, robustness, and training stability over strong robust RL baselines.

Autonomous unmanned aerial vehicles (UAVs) increasingly rely on reinforcement learning (RL) for navigation, yet global navigation satellite system (GNSS) spoofing attacks can induce out-of-distribution observation shifts that corrupt value estimation and degrade mission performance. Existing robust RL methods often lack generalization to unseen attacks. To address this, we propose a curriculum-guided adaptation framework that progressively exposes a robust policy to gradient-based adversarial observation perturbations of increasing intensity while aligning temporal-difference (TD) error distributions across curriculum stages. By preserving TD-error consistency rather than targeting specific attack models, the framework promotes resilience across attack conditions. We further derive a TD-space generalization certificate that bounds performance degradation when the TD-error distribution under a test-time attack remains sufficiently close to that of the final curriculum stage. We evaluate the framework in a UAV deconfliction environment with dynamic 3D obstacles under unseen fixed and dynamic GNSS spoofing attacks. Under fixed spoofing, the curriculum-adapted (CurAd) policy achieves approximately 99-100% mission success across evaluated traffic densities, compared with 20-24% for the best-performing robust baseline, while also outperforming the non-robust vanilla baseline. Under dynamic obstacle-luring spoofing, CurAd achieves the highest episodic rewards and reduces median mission completion steps by up to 22% relative to the best-performing robust RL baseline. These findings demonstrate that adversarial curriculum adaptation transfers resilience from synthetic perturbations to unseen GNSS spoofing attacks without attack-specific retraining, enhancing robust autonomous UAV navigation.