  In this work, an advanced deep reinforcement learning architecture is used to
train neural network agents playing atari games. Given only the raw game
pixels, action space, and reward information, the system can train agents to
play any Atari game. At first, this system uses advanced techniques like deep
Q-networks and dueling Q-networks to train efficient agents, the same
techniques used by DeepMind to train agents that beat human players in Atari
games. As an extension, plastic neural networks are used as agents, and their
feasibility is analyzed in this scenario. The plasticity implementation was
based on backpropagation and the Hebbian update rule. Plastic neural networks
have excellent features like lifelong learning after the initial training,
which makes them highly suitable in adaptive learning environments. As a new
analysis of plasticity in this context, this work might provide valuable
insights and direction for future works.
