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In this paper, we propose stochastic policy based wireless energy harvesting in green cognitive radio network.
Policy based methods can learn explicitly an optimal stochastic policy [16], which is our goal.
Hence, the proposed stochastic policy is capable of iteratively finding the optimal.
As expected, the optimal policy demonstrates the same performance with the stochastic policy.
This method introduces a new architecture to the actor, and its training is roughly based on a stochastic policy gradient algorithm presented recently.
In the"Stochastic policy based wireless energy harvesting in green cognitive radio network" section, we illustrate the eco-friendly, low carbonate, energy harvesting capable CR network technique which this paper proposes.
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The stochastic policies are better in terms of the power delay trade-off than heuristic policies [18].
A paper on controllable Markov decision model provides heuristic and stochastic policies with linear programming optimization [43].
However, these schemes focused on analyzing the fundamental energy harvesting functions with the network coding and did not consider the specific relationship between the energy harvesting and the optimal stochastic policies in the network coding capable green CR network.
It can be seen that a perfectly rational agent with α→∞ is able to pick out the optimal action which is a deterministic policy in the case of a single optimum—, whereas finitely rational agents have stochastic policies with non-zero probability of picking a sub-optimal action.
The stochastic LP policy (SLP) selects stochastically with respect to "probabilities" x_{j,i,k}/sum_{k} x_{j,i,k}. .
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