mlsanityData to 14 Oct 2026

Learning To Play Atari Games Using Dueling Q-Learning and Hebbian Plasticity

arXiv 2405.13960 ↗First posted 22 May 2024cs.AI cs.LGcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 20 references that are in the map. First posted on arXiv 22 May 2024.

Builds on

6 of 20 references.

References · 6

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  3. 2015Prioritized Experience Replaycited by 1,099
  4. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 786
  5. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  6. 2018Differentiable plasticity: training plastic neural networks with backpropagationcited by 84

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  2. 2018Reward learning from human preferences and demonstrations in Atari
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  4. 2024FDQN: A Flexible Deep Q-Network Framework for Game Automation
  5. 2024Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 Games
  6. 2025Deep Reinforcement Learning in Applied Control: Challenges, Analysis, and Insights
  7. 2025Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations
  8. 2025Learning Game-Playing Agents with Generative Code Optimization