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To our knowledge, this is the first attempt to apply causal analysis in a reinforcement learning setting without strict restrictions on the number of states.
The results show faster learning with the presented approach as opposed to learning the control policy from scratch for this new UAV design created by modifications in a conventional quadcopter, i. e., the addition of more degrees of freedom (4-actuators in conventional quadcopter to 8-actuators in tilt-rotor quadcopter).
A significant challenge in developing AI that can generalize well is designing agents that learn about their world without being told what to learn, and apply that learning to challenges with sparse rewards.
Transfer learning is an important new subfield of multiagent reinforcement learning that aims to help an agent learn about a problem by using knowledge that it has gained solving another problem, or by using knowledge that is communicated to it by an agent who already knows the problem.
We develop a general reinforcement learning framework for mean field control (MFC) problems.
The successful application of general reinforcement learning algorithms to real-world robotics applications is often limited by their high data requirements.
This is the first variational regret bound for the general reinforcement learning setting.
Macro actions, a sequence of primitive actions, have been studied to diminish the dimensionality of the action space with regard to the time axis.