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The cleanest solution from a theoretical point of view is to switch over to a hierarchical framework, some framework that supports temporal abstraction. My favourite one is the options framework as formalised by Sutton, Precup and Singh. The basic idea is that the things that you consider "actions" for your agents become "options", which are "large actions" ...


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I think there is an intersection. There are problems that are in reinforcement learning and in learning in multi-agent systems. There are problems in reinforcement learning, but not exactly in multi-agent systems. And there is learning in multi-agent systems that is not through reinforcement learning. For sort you can say: multi-agent reinforcement learning. ...


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it can be either. If you consider the lack of reward as "penalty" then getting 0 reward is bad. if you use a value estimator through a neural network, the range of rewards will dictate the squashing function you use for the output layer


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In MORL the reward component is a vector rather than a scalar, with an element for each objective. So if we are using a multiobjective version of an algorithm like Q-learning, the Q-values stored for each state-action pair will also be vectors. Q-learning requires the agent to be able to identify the greedy action in any state (the action expected to lead ...


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An agent is a concept, which can have slightly different meanings, abilities or instantiations depending on the context. However, given the purpose of this website, I will use and refer to the definition of agent commonly used in artificial intelligence. An agent is anything that can be viewed as perceiving its environment through sensors and acting upon ...


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