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In reinforcement learning, an agent is usually fully autonomous and independent. It executes actions on the environment, but no other agent can control, explore or command this agent.

In multi-agent reinforcement learning, can one agent explore, command or communicate with other agents?

Would communication between agents somehow be different from the reciprocal execution of each other's actions between agents?

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    $\begingroup$ What do you mean by "reciprocal execution of each other's actions between agents"? $\endgroup$ – nbro Jul 15 '19 at 21:15
  • $\begingroup$ "Reciprocal execution" - is it possible that one agent act as the environment of the other agent and hence exposes actions for command itself and similarly the other agent can act as the environment for the first agent. Just explaining what I meant initially. I am still digesting Manuel's answer and I now have my own thoughts, I will detail them later. The solution lies in the notion of the "action" - can action which returns callback (from environment back into action) be an action for reinforcement learning setting? $\endgroup$ – TomR Jul 15 '19 at 22:10
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    $\begingroup$ @TomR What do you mean "hence exposes actions for command itself", do you mean that the first agent (the environment) decides which actions are available for the second agent? If this is your question, then I will remove "In multi-agent reinforcement learning, can one agent explore, command or communicate with other agents?", because this is a sightly different question. $\endgroup$ – nbro Jul 15 '19 at 23:10