What value (either practical or philosophical, as opposed to purely mathematical), if any, do you see in this result, or in the result about episodic environments?

There are plenty of applications of reinforcement learning where it is plausible to assume that the environment is ergodic (that is, the agent can't "die" or fall into traps that permanently result in low rewards) or episodic. The Google DQN Atari game agent, for instance, operates in an episodic environment, therefore, stochastic action selection is acceptable.

Of course, this is not suitable for an AGI operating in an unconstrained physical environment.