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Self-managed action learning is a variant of Action Learning that dispenses with the need for a facilitator of the action learning set, including in virtual and hybrid settings. [25] [26] [27] There are a number of problems, however, with purely self-managed teams (i.e., with no coach).
Given a training set consisting of examples = (,, ′), where , ′ are observations of a world state from two consecutive time steps , ′ and is an action instance observed in time step , the goal of action model learning in general is to construct an action model , , where is a description of domain dynamics in action description formalism like STRIPS, ADL or PDDL and is a probability ...
Dominating set, a.k.a. domination number [3]: GT2 NP-complete special cases include the edge dominating set problem, i.e., the dominating set problem in line graphs. NP-complete variants include the connected dominating set problem and the maximum leaf spanning tree problem. [3]: ND2 Feedback vertex set [2] [3]: GT7
Similar to reinforcement learning, a learning automata algorithm also has the advantage of solving the problem when probability or rewards are unknown. The difference between learning automata and Q-learning is that the former technique omits the memory of Q-values, but updates the action probability directly to find the learning result.
For the following definitions, two examples will be used. The first is the problem of character recognition given an array of bits encoding a binary-valued image. The other example is the problem of finding an interval that will correctly classify points within the interval as positive and the points outside of the range as negative.
The worked-example effect is a learning effect predicted by cognitive load theory. [1] [full citation needed] Specifically, it refers to improved learning observed when worked examples are used as part of instruction, compared to other instructional techniques such as problem-solving [2] [page needed] and discovery learning.
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The mountain car problem, although fairly simple, is commonly applied because it requires a reinforcement learning agent to learn on two continuous variables: position and velocity. For any given state (position and velocity) of the car, the agent is given the possibility of driving left, driving right, or not using the engine at all.