Building an adaptive tokenomics model using reinforcement learning algorithms to ensure long-term economic sustainability of crypto projects.

The article examines the robustness of algorithmic tokenomics models under conditions of extreme volatility in cryptocurrency markets, using the collapse of the Terra/Luna ecosystem (2022) and liquidity crises in 2019–2023 as empirical material. It is shown that static, deterministic monetary supply control loops exhibit a systemic vulnerability: under abrupt shifts in market conditions, fixed rules for issuance and liquidity withdrawal lag, do not account for the distribution of scenarios, and incorrectly process nonlinearities in stress regimes, which increases the probability of the system entering unstable trajectories. As an alternative, an adaptive economic architecture is substantiated, in which regulatory functions are implemented by an intelligent agent based on deep reinforcement learning (DRL). Proximal Policy Optimization (PPO) is selected as the baseline algorithm, ensuring stable policy updates under an optimization step constraint, which is critical for preventing cascading destabilization. The state space and action set of the regulator agent are formalized, enabling the dynamics of key macro-parameters of tokenomics to be described as a controlled stochastic process. Simulations in cadCAD demonstrate the advantage of DRL stochastic policies compared to PID controllers in maintaining the peg and supporting liquidity under conditions of partial observability and noisy perturbations. Additionally, current requirements of legal regulation of digital financial assets are taken into account, and approaches are proposed for integrating DRL agents into smart contracts with on-chain execution, controlled access to data, and preservation of verifiability and reproducibility of the mechanism.