Use of relaxed stochastic controls in reinforcement learningRems, Jan (Avtor) Agram, Nacira (Mentor) Košir, Tomaž (Komentor) reinforcement learningexplorationstochastic control theoryrelaxed controlsdynamical programmingoptimal investment strategyIn this work, we investigate how relaxed stochastic controls are used for exploration in continuous time and space reinforcement learning. The environment $X^u$ is modeled by a stochastic differential equation controlled by control $u$, while the value function $V^u$ is an infinite horizon performance functional. For relaxed control distribution $\pi$ we introduce relaxed versions of environment $X^{\pi}$ and value function $V^{\pi}.$ In a special linear-quadratic case the optimal control distribution turns out to be Gaussian with mean depending on the current state, and variance depending on exploration weight parameter. A reinforcement learning algorithm for optimal investment strategy in a simple model of the financial market with the infinite horizon is developed and tested.20212021-09-16 08:15:15Magistrsko delo/naloga130550sl