Risk-Constrained Reinforcement Learning With Augmented Lagrangian Multiplier for Portfolio Optimization

We explored the application of Risk-averse Reinforcement Learning (Risk-averse RL) in Constrained Markov Decision Process (CMDP) in optimizing investment portfolios, incorporating constraints assessment. The investment portfolio must be always constrained with risk characteristics by investors and regulators. Therefore, the hard constraint is necessary for the practical Portfolio optimization. Moreover, traditional portfolio optimization techniques lack flexibility to model complex dynamic financial market. To address this issue, Augmented Lagrangian Multiplier (ALM) was employed to enforce constraints on the agent, mitigating the impact of risk in the decision process. Our proposal of the risk-constrained RL algorithm demonstrated no constraint violations during the testing phase, and outperformance compared to other Risk-averse RL algorithms, fulfilling our primary goal. This suggests that incorporating a risk-constrained RL technique holds promise for portfolio optimization, particularly for risk-averse investors.