Learning To Play Atari Games Using Dueling Q-Learning and Hebbian Plasticity
Md Ashfaq Salehin·May 22, 2024
Summary
The paper presents a deep reinforcement learning architecture that combines dueling Q-networks and plastic neural networks for improved performance in Atari games. Inspired by DeepMind's achievements, the system employs techniques like Dueling Double DQN for efficiency and incorporates plastic neural networks, which allow for lifelong learning through backpropagation and Hebbian update rules. The study compares plasticity-based agents with traditional DQN methods, highlighting the potential of plasticity for enhancing learning and addressing issues like catastrophic forgetting and overfitting. The research also explores the use of the Arcade Learning Environment (ALE) as a benchmark, with future directions suggesting improvements in hyperparameters, plasticity rules, and integration with more advanced techniques. Overall, the work showcases the potential of plasticity in reinforcement learning for complex tasks and problem-solving.
Introduction
Background
DeepMind's impact on reinforcement learning
Atari games as a benchmark for AI research
Objective
To develop a novel architecture combining dueling Q-networks and plastic neural networks
Investigate plasticity's potential for improved performance and lifelong learning
Method
Architecture
Dueling Double DQN
Description and benefits of the dueling architecture
Double Q-learning to reduce overestimation bias
Plastic Neural Networks
Integration of backpropagation and Hebbian update rules
Comparison with traditional DQN networks
Data Collection
Atari Learning Environment (ALE) setup
Game selection and experimental design
Data Preprocessing
State representation and preprocessing techniques
Experience replay for efficient learning
Experiments
Performance Evaluation
Comparisons between plasticity-based agents and DQN
Metrics: average return, learning speed, and stability
Catastrophic Forgetting and Overfitting
Analysis of plasticity's impact on these issues
Hyperparameter Optimization
Exploration of different hyperparameters for plasticity and dueling mechanisms
Results and Discussion
Plasticity-based agents' performance improvements
Advantages and limitations of the proposed architecture
Lessons learned and implications for future research
Future Directions
Enhancing plasticity rules for better learning
Integration with advanced techniques (e.g., transfer learning, meta-learning)
Scaling to more complex games and environments
Conclusion
Summary of key findings
The potential of plastic neural networks in reinforcement learning for complex tasks
Implications for the broader AI community
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
How does the system improve performance in Atari games compared to traditional DQN methods?
What techniques does the paper's architecture employ from DeepMind's achievements?
What deep reinforcement learning architecture is discussed in the paper?
What is the primary focus of the study when comparing plasticity-based agents with DQN methods?