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This paper investigates the use of deep reinforcement learning (DRL) to solve the dynamic spectrum access problem. Specifically, we examine the scenario where multiple discrete channels are shared by different types of nodes which lack the ability to communicate (with other node types) and do not have a priori knowledge of the other nodes' behaviors. Each node's objective is to maximize its own long-term expected number of successful transmissions. The problem is formulated as a Markov Decision Process (MDP) with unknown system dynamics. In order to overcome the challenge of an unknown environment combined with a prohibitively large transition matrix, we apply two specific DRL approaches: The Deep Q Network (DQN) and The Double Deep Q Network (DDQN). Additionally, we also introduce techniques to improve DQNs including an eligibility trace, prior experience and the “guess process”. We …