This allows for local interpretation of the agent's intention based on its behavioural policy.

We study risk-sensitive reinforcement learning (RL) based on the entropic risk measure. Although existing works haveestablished non-asymptotic regret guarantees for this problem, they leave open an exponential gap between the upper and lower bounds. We identify the deficiencies in existing algorithms and their analysis that result in such a gap. To remedy these deficiencies, we investigate a simple transformation of the risk-sensitive Bellman equations, which we call theexponentialBellmanequation.

The robot could learn from these demonstrations to complete the tasks autonomously. For IL algorithms to be useful, however, they must be able to learn how to perform tasks from few demonstrations. A domestic robot wouldn't be very helpful if it required thirty demonstrations before it figured out that you are deliberately washing your purple cravat

By identifying an instrumental variable correlated with the variableX butunrelated tothe confounders, researchers can isolate the exogenous variation inX and estimate acausal relationship betweenX andY.