g.6: model of reinforcement learning.[8] Q-learning,Dual Reinforcement learing, Team-partitioned opaque-transition Reinforcement learing, Collaborative Reinforcement learing are Algorithms of Reinforcement learing. Fuzzy logic: Fuzzy logic system is multivalued function i.e derived from fuzzy set values to deal with approximate reasoning. The use of fuzzy logic is suitable for decision making. Fuzzy logic is applied to deal with imprecise information rather than precise.the use of fuzzy logic is appropriate method for decision process as it describes sytem intuitively using linguistic variables.Fuzzy can reduce development time as compared to other techniques. Advantages of FLS: i. FLS is simple as less computation is required. ii. FLS can endure imprecise and unreliable data.[13] Fuzzy logic system can be applied in deployment of wireless sensor network. FLS in deployment is previously used by Mathur et al.[12] their system was successful to achieve fast and stable deployment with maximum coverage.It also optimizes energy consumption of sensor nodes during deployment.But this system does not remove overlaping of nodes .This drawback of system should be removed in future. Amjad Osmani [9] have presented the sensor deployment to achieve enhanced coverage after redeployment of sensor nodes using fuzzy sensor placement based on neighbor state.But still there is need to work on energy consumption and intersection of nodes in this technique. Fig.7 deployment using FLS There are four components of fuzzy logic system. i. Fuzzification module: It changes the system inputs, which are crisp numbers, into fuzzy sets. All fuzziness for fuzzy sets is calculated by member functions. ii. Knowledge Base: It stores IF-THEN rules provided by experts. iii. Inference engine: It recreates the human making so as to think procedure fuzzy surmising on the inputs and IF-THEN guidelines. iv. Defuzzification: It changes the fuzzy set obtained by the inference engine >