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This paper presents the ideal approach to how to minimize the time taken by reinforcement learning to train the model. Similar to Computer vision the progress in reinforcement learning is not influenced by new ideas but mostly by the computation, large data, infrastructure and efficiency of algorithm. These 4 things only influenced the reinforcement learning RL model. How much time it will take to...
Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC-called a Tensor Processing Unit (TPU)-deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second...
One of the main drawbacks in Real-time strategy (RTS) games is that the built-in artificial intelligence (or gamebots) tend to lag behind human players. To make gamebots perform like human players, gamebots should try to find best action from the Knowledge (training data) for each time-stamp and should be able to play game against every opponent. To achieve this end in this paper we propose a learning...
Bloat is a problem that occurs when there is no advancement in fitness measure, but the size of the tree grows exponentially. Bloat eventually increases the time required to reach the optimal solution. To overcome this defect, Crossover and Mutation with Intelligence technique is proposed. We also used double tournament, in which we apply two tournaments on the basis of size and fitness respectively...
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