Learning To Play Atari Games Using Dueling Q-Learning and Hebbian Plasticity
This work addresses improving reinforcement learning for game-playing agents, but it is incremental as it builds on existing methods like DeepMind's approaches.
The paper tackled training neural network agents to play Atari games using raw pixels, actions, and rewards, achieving results comparable to human-level performance with techniques like deep Q-networks and dueling Q-networks, while also analyzing the feasibility of plastic neural networks with Hebbian updates for lifelong learning.
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.