A Transdiagnostic Space of Disorder Like Phenotypes in Reinforcement Learning Agents

Modelling psychological disorders in artificial agents offers both a testbed for computational psychiatry and a lens on the failure modes of affective control.

Prior work induces one or two disorders in a reinforcement learning (RL) agent by hand-tuned reward shaping, labels the behaviour post hoc, and reports single runs.

We recast disorder modelling as dose-controllable manipulation of cognitive appraisal signals in an appraisal-guided PPO agent, expressing seven disorders (anxiety, mania, obsessive-compulsive checking, depression, impulsivity, addiction, and post-traumatic stress) each as a single knob grounded in a computational psychiatry account, with each symptom measured by a preregistered assay mapped to a recognised paradigm.

Across more than a thousand runs (10 seeds, four controls, 95% confidence intervals) every disorder shows a graded, monotone dose-response that no control reproduces.

Beyond these induced effects, three findings emerge that were not written into the reward: the disorders self-organise into a two-dimensional affective space in which mania mirrors anxiety; removing a knob remits reward distortion disorders (mania, checking, addiction) but not avoidance disorders (anxiety, PTSD), which instead recover under a graded exposure curriculum; and two simultaneous knobs interact nonadditively, yielding testable comorbidity predictions.

Appraisal weights thus parameterise a controllable space of affective phenotypes in which the same knobs that induce a disorder can model its treatment.

We also show that three disorder knobs (depression, addiction, anxiety) transfer to a three-dimensional pixel environment (MiniWorld) with a standard convolutional agent and no appraisal critic, with cross-assay dissociation confirmed across both domains, indicating the framework is not specific to grid worlds or to PPO's appraisal critic.