Cryptocurrency is a new type of asset that has emerged as a result of advancements in financial technology and has created an excellent opportunity for research. Cryptocurrency price prediction is difficult due to price volatility and momentum. Worldwide, hundreds of cryptocurrencies are used. This article provides three types of Recurrent Neural Network (RNN) algorithms used to predict the prices of three types of cryptocurrencies, namely Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models show excellent predictions based on the mean absolute error in percentage (MAPE). The results obtained from these models show that the closed recurrent unit (GRU) performed better in prediction for all types of cryptocurrency than the long-term memory models (LSTM) and two-way LSTM ( biLSTM). Therefore, it can be considered as the best algorithm. GRU has the most accurate prediction for LTC with [yMAPE percentages of 0.2454%, 0.8267%, and 0.2116% for BTC, ETH and LTC, respectively

Cryptocurrency, Reinforcement Learning, Dataset, LSTM