The agent operates within a closed-loop CAS framework, meaning:
Its mutation choices alter the systemic state
Emergent behaviors (e.g., fold bifurcations, catalytic innovation) affect the reward landscape
The environment becomes increasingly non-stationary, emulating natural evolutionary tension
The system may exhibit phenomena such as:
Attractor collapse: agents discover highly fit states but overexplore them, reducing diversity
Adaptive bifurcation: RL-driven exploration induces novel folds or binding modes
Evolutionary arms race: if a co-evolving environment is simulated (e.g., polymer substrate variants)
5. Emergence of Mutational Heuristics
Over time, the RL agent develops implicit heuristics that mirror real-world molecular logic:
Preference for stabilizing mutations near structural cores
