… In this paper, instead of using conventional algorithms, we present the usage of DQN (Deep
Q-Network), a reinforcement learning algorithm, to solve the path planning problem. The …

… Our research provides insights into the potential of deep quantum learning to solve a maze
… agent should take by trained deep Q-network (DQN). The blue point is the exit of the maze …

… a maze, suggesting that improvements to DQN the approach are also plausible solutions
to the mazesolving … will be provided in the Deep Q-Network section later in this chapter. …

… N-DQN model is implemented in this paper with examples of maze finding and ping-pong
as examples of delayed reward system, where delayed reward occurs, which makes general …

… Efficient maze solving plays a key role in some branches of Artificial Intelligence [25]. The
Q-… Multi-agent deep Q network to enhance the reinforcement learning for delayed reward …

… We hypothesized that the simplicity of the maze we use in this work in which the agent …
, people tend to use the Deep Q Network, also called deep Qlearning, to solve the problem. …

… replay strategy was also used in the weighted double deep Q-Network (WDDQN) in [38] in
order … -v2, and a maze traversal task implemented locally. For testing we use the DQN and the …

… Deep Reinforcement Learning (MARL) training using agent communication and the model-free
Deep Q-Network … It refers to an accurate representation of the maze cells in our maze (the …

… maze game in which the player (mouse) needs to reach the goal (cheeze) in the least
possible steps through a maze… Our proposed method, Q-matrix pretrained dueling deep Q-network …

… solve these types of problems using a new bio-inspired neural architecture that combines a
modulated Hebbian network (MOHN) with deep Q-network (… Bird’s eye view of maze used in …