My way of putting much the same idea was:
you could, in theory, train a Solomonoff Induction-based reinforcement learning agent to produce arbitrary finite sequences of actions (non-self-destructive ones anyway) in response to specified sets of finite sense data - assuming you are allowed to program its reward function and give it fake memories dating back from before it was born.
Basically Solomonoff Induction is a powerful learning mechanism, and with sufficient time and test cases, you could configure an agent based on it to behave in an arbitrary way[*] in response to any finite sense-stream after its "birth" - by giving it sufficient pre-birth training "memories" - which laboriously say: "if you see this, do this, and don't do this or this or this" - for every possible bunch of observations, up to some finite length limit.
I call this sort of thing universal action - and I think reinforcement learning systems are capable of it.
Daniel Dewey, 'Learning What to Value'
Abstract: I.J. Good's theory of an "intelligence explosion" predicts that ultraintelligent agents will undergo a process of repeated self-improvement. In the wake of such an event, how well our values are fulfilled will depend on whether these ultraintelligent agents continue to act desirably and as intended. We examine several design approaches, based on AIXI, that could be used to create ultraintelligent agents. In each case, we analyze the design conditions required for a successful, well-behaved ultraintelligent agent to be created. Our main contribution is an examination of value-learners, agents that learn a utility function from experience. We conclude that the design conditions on value-learners are in some ways less demanding than those on other design approaches.