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Once parsed, the gameplay data can be plugged right into those reinforcement-learning reward functions.
We define the different subMDPs, with state and action spaces and tentative proposals for reward functions.
The kit also includes reward functions for collision avoidance, minimizing destination distance, and maximizing adherence to the road.
An efficient way to represent the CPTs (and factored reward functions) is through algebraic decision diagram (ADD) [9].
Understanding the design principle of reward functions is a substantial challenge both in artificial intelligence and neuroscience.
Policy Reuse was introduced, and its effectiveness was previously demonstrated, in problems with different reward functions in the same state and action spaces.
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Thus, the reward function is.
Fig. 3 Reward function as a table.
The reward function that we use for our model is in form of Eq. (7), which is a summation of fairness reward function and network utilization reward function.
Based on the two functions, a reward function is formulated.
Thus, both aspects should be covered by the reward function.
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