VaRL

VaRL is a class of reinforcement learning methods that incorporate variance-aware or risk-sensitive objectives into the standard RL framework. By taking into account not only the expected return but also its variability, VaRL aims to produce policies that are reliable under uncertainty.

The core idea is to optimize a risk-adjusted objective, such as a mean-variance trade-off or conditional value at risk (CVaR), rather than solely maximizing expected returns. Variance or risk terms can be embedded in the loss function for value estimation or in the policy objective.

In practice, VaRL can be realized within multiple RL paradigms. In value-based methods, a variance term is estimated for returns and added to the Bellman error. In policy-gradient and actor-critic methods, risk terms modify the policy gradient or the critic update, and distributional approaches can be used to model the full return distribution.

Variants include mean-variance VaRL, CVaR VaRL, and risk-averse actor-critic architectures. Some approaches use ensemble models or quantile regression to better capture tail risk.

Applications span finance for risk-aware trading, robotics and autonomous systems operating in stochastic environments, energy management, and other domains where stable performance is valued over occasional high returns.

Limitations include higher computational cost, sensitivity to risk-aversion parameters, and potential over-conservatism that can slow learning or reduce exploration.

See also reinforcement learning, risk-sensitive reinforcement learning, distributional reinforcement learning, and CVaR.