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The exploration reward (also called exploration bonus) methods convert the exploration-exploitation dilemma into a balance of exploitations. That is, instead of trying to get the agent to balance exploration and exploitation, exploration is simply treated as another form of exploitation, and the agent simply attempts to maximize the sum of ...
The trade-off between exploration and exploitation is also faced in machine learning. In practice, multi-armed bandits have been used to model problems such as managing research projects in a large organization, like a science foundation or a pharmaceutical company .
The other approach is contextual ambidexterity, which uses behavioral and social means to integrate exploitation and exploration at the organizational unit level. [17] [18] Contextual ambidexterity is a balanced type that takes a mid-level position between exploitation and exploration, also known as parallel structures or hybrid strategies.
These two objectives may be partly in conflict. In the context of reinforcement learning, this is known as the exploration-exploitation trade-off (e.g. Multi-armed bandit#Empirical motivation). Dual control theory was developed by Alexander Aronovich Fel'dbaum in 1960.
The exploration vs. exploitation trade-off has been most thoroughly studied through the multi-armed bandit problem and for finite state space Markov decision processes in Burnetas and Katehakis (1997).
Outgoing U.S. President Joe Biden will sign an executive order on Sunday aimed at prioritizing government resources to help economically distressed American communities - a day before he leaves ...
For every new rule, President Donald Trump plans to kill 10 old ones. That's the thrust of the president's latest executive order, signed Friday, called "Unleashing Prosperity Through Deregulation
Bayesian optimization of a function (black) with Gaussian processes (purple). Three acquisition functions (blue) are shown at the bottom. [8]Bayesian optimization is typically used on problems of the form (), where is a set of points, , which rely upon less (or equal to) than 20 dimensions (,), and whose membership can easily be evaluated.