Continuous-Time Reinforcement Learning for Asset–Liability Management

Battle Count: 68

The paper is primarily focused on Asset-Liability Management rather than direct quantitative trading strategies. However, it is highly relevant to quantitative finance broadly: the continuous-time RL framework, LQ control formulation, and model-free policy learning are directly applicable to portfolio optimization, dynamic hedging, and risk management. The methodology of learning optimal control policies without environment model estimation is transferable to trading contexts. The entropy-regularized exploration and actor-critic architecture share foundations with algorithmic trading RL approaches. The relevance is moderate (5/10) because ALM is an adjacent but distinct problem from active trading, though the techniques and frameworks are broadly applicable.

The algorithm is moderately complex. The core parameterization is relatively simple (quadratic value function, linear Gaussian policy), avoiding the need for deep neural networks. The continuous-time formulation requires careful handling of SDEs and Itô calculus. The adaptive and scheduled exploration mechanisms add some complexity but are well-specified in the pseudocode. The discretization is applied only at the implementation stage. Key challenges include: (1) correctly implementing the continuous-time policy gradient updates with proper noise handling; (2) tuning the exploration schedules (bₙ, c_γ); (3) ensuring numerical stability through parameter projections; (4) the convergence proof requires understanding of stochastic approximation theory. The paper provides complete pseudocode and update rules, reducing implementation ambiguity. No code is provided, which increases practical implementation effort.

The paper provides detailed pseudocode (Algorithm 1), explicit update rules (Eqs. 16-18), all hyperparameter settings (learning rate, exploration schedules, projection bounds, parameter ranges), and the experimental setup (200 scenarios, 20,000 episodes, Δt=0.01). However, no code repository is mentioned, and results are presented only via figures (Figure 1 and Figure 2) without numerical tables. The randomized parameter ranges are specified, enabling replication of the simulation environment. The theoretical convergence proof is provided in full.