Adversarial Reinforcement Learning for Robust Planetary Landing
Planetary landing controllers learned via deep RL are usually randomized with independent disturbances, yet real mission perturbations are temporally correlated. This paper proposes adversarial RL where a second agent generates smooth, physically bounded disturbance sequences to stress-test a 3-DOF lunar lander, framing training as an asymmetric zero-sum game. A staged curriculum (nominal, stochastic, adversarial) aims to expose hidden failure modes that standard uncorrelated randomization misses. Early results suggest the adversarially hardened policy resists larger out-of-distribution perturbations better, though at some cost in low-disturbance accuracy and with limited statistical validation.
The paper presents a conceptually sound and practically motivated application of adversarial RL to planetary landing robustness. Its staged curriculum and emphasis on temporally correlated disturbances are well-aligned with real aerospace GNC needs. However, the experimental campaign remains preliminary: the dynamics are restricted to 3-DOF, sample sizes and statistical tests are limited, and the authors themselves note that the adversarially trained policy partially overfits to severe disturbances and sacrifices mild-condition precision. As a result, the work is better viewed as a promising proof-of-concept than as a validated robust control synthesis method.
The strongest aspect is the problem framing and methodology. The distinction between independent stochastic perturbations and temporally correlated mission-relevant disturbances is well-articulated, and casting disturbance generation as a sequential zero-sum adversary is a natural fit for safety-critical GNC. The reward design—using a streamline-based progress term $r_t^L$ for the lander and a sparse terminal error $R_{\mathrm{adv}}$ for the adversary—is sensible, and the staged curriculum with bounded incremental perturbations provides stable optimization dynamics. Figures 3 and 4 offer clear visual evidence that adversarial training exposes weaknesses missed by stochastic fine-tuning and improves tail-end robustness.
The empirical evaluation is too limited to support strong conclusions. The study relies on a simplified 3-DOF point-mass model without attitude coupling, and the Monte Carlo campaign uses only one hundred rollouts per scenario without reporting confidence intervals or significance tests, making it impossible to judge whether the apparent gains in Figure 4 are statistically reliable. The adversarially trained policy also shows clear signs of specialization: the authors explicitly note that it demonstrates stronger robustness at higher disturbance levels, but that 'some degradation appears in lower-perturbation regions,' indicating overfitting to the current adversary rather than uniform robustness.
Additionally, key implementation details are omitted that would be necessary to reproduce or extend the work. PPO hyperparameters, network architectures, simulation timestep, random seeds, and exact perturbation bounds are not reported, and no code or environment specification is released. The asymmetric zero-sum formulation, while intuitive, receives no theoretical analysis—existence of equilibria, convergence guarantees, or bounds on the duality gap are not discussed—leaving the training protocol under-constrained from a game-theoretic standpoint.
The evidence supports the narrower claim that an adversary can find harder disturbance sequences than independent random sampling, as shown by the degradation of the stochastically fine-tuned policy under adversarial evaluation (Figure 3). However, the broader claim of improved robustness and stronger generalization remains tentative; the OOD tests consist of larger scalar perturbations rather than structurally different disturbance models, and there is no quantitative ablation isolating the contribution of the zero-sum formulation from simply training longer or with larger stochastic bounds. Comparisons to related work are fair but superficial: while the authors correctly position their approach against domain randomization and cite the seminal adversarial RL work of Pinto et al., they do not empirically compare against standard robust RL baselines or clarify what distinguishes their asymmetric reward structure from existing frameworks.
Reproducing this work would be difficult based on the manuscript alone. Although the 3-DOF translational dynamics and reward terms are written out, critical training details are missing: PPO hyperparameters, network sizes, learning rate schedules, the length of each curriculum stage, the simulation timestep, and the exact numerical bounds on disturbances are not provided. The authors use a recurrent adversary but do not describe the recurrent architecture or observation encoding. There is no mention of random seeds, code availability, or environment open-sourcing. Without these elements, independent verification or fair comparison is essentially impossible.
Deep reinforcement learning (DRL) policies for planetary landing and other aerospace guidance, navigation, and control tasks are commonly trained under randomized disturbances to improve robustness. However, typical stochastic perturbation sampling assumes independent variations, whereas real mission disturbances often exhibit spatial and temporal correlation and cannot be reproduced through independent random draws. This work investigates adversarial reinforcement learning to discover challenging off-nominal conditions. A secondary agent generates physically constrained perturbations that degrade the performance of a landing controller, forming an asymmetric sequential zero-sum game. Training proceeds in three stages: baseline controller learning, robustness enhancement with stochastic perturbations, and an iterative adversarial phase in which the controller and disturbance generator are alternately optimized. Preliminary experiments in a three-degree-of-freedom landing environment show that adversarial disturbances reveal vulnerabilities missed by conventional randomization and provide early indications that, with a more extensive training campaign, the approach can yield improved robustness and stronger generalization under previously unseen perturbation regimes.
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