The Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural networks (MLPNN) model can be used for the estimation of angular displacement and movement angular velocity of the elbow with good accuracy.
Model-based reinforcement learning uses models to plan, where the predictions and policies of an agent can be improved by using more computation without additional data from the environment, thereby improving sample efficiency. However, learning accurate estimates of the model is hard. Subsequently, the natural question is whether we can get similar benefits as planning with modelfree methods. Experience replay is an essential component of many model-free algorithms enabling sample-efficient learning and stability by providing a mechanism to store past experiences for further reuse in the gradient computational process. Prior works have established connections between models and experience replay by planning with the latter. This involves increasing the number of times a mini-batch is sampled and used for updates at each step (amount of replay per step). We attempt to exploit this connection by doing a systematic study on the effect of varying amounts of replay per step in a well-known modelfree algorithm: Deep Q-Network (DQN) in the Mountain Car environment. We empirically show that increasing replay improves DQN's sample efficiency, reduces the variation in its performance, and makes it more robust to change in hyperparameters. Altogether, this takes a step toward a better algorithm for deployment.
despite of the abundant resource potential and availability of conventional resources like oil and gas in many countries is moving toward the sustainable energy like solar power. Even though these country's has a substantial amount of installed capacity largely based on renewable energy like hydropower, but these resources yield fluctuates greatly during the drought season, forcing to rely on expensive emergency thermal units which again considerably affects the environmental balance but by implementing solar power resources substantial difference can be brought in the environmental circumstances. This paper presents simulation and study of PV cells using the generic model on PSPICE platform. Using PSPICE circuit simulator voltage and current variation can be verified under different a set of condition. Diode based models are used to conduct the studies which are classified on the basis of number of diodes. This paper provides a comparative behavioral study of different models under varying condition of temperature, diode parameter, solar insolation, and etc. This study helps in sketching out the performance evaluation under different circuit topology and control strategy.
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