Towards Sample-efficient Episodic Control With DAC-ML

Abstract

The sample-inefficiency problem in Artificial Intelligence refers to the inability of current Deep Reinforcement Learning models to optimize action policies within a small number of episodes. Recent studies have tried to overcome this limitation by adding memory systems and architectural biases to improve learning speed, such as in Episodic Reinforcement Learning. However, despite achieving incremental improvements, their performance is still not comparable to how humans learn behavioral policies. In this paper, we capitalize on the design principles of the Distributed Adaptive Control (DAC) theory of mind and brain to build a novel cognitive architecture (DAC-ML) that, by incorporating a hippocampus-inspired sequential memory system, can rapidly converge to effective action policies that maximize reward acquisition in a challenging foraging task.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyfreire2020towards

Related papers

• Sequential Memory Improves Sample And Memory Efficiency In Episodic Control (2021)0.00
• Sample-efficient Reinforcement Learning With Maximum Entropy Mellowmax Episodic Control (2019)0.00
• Continuous Episodic Control (2022)2.26
• Memory-efficient Episodic Control Reinforcement Learning With Dynamic Online K-means (2019)0.00
• Integrating Episodic Memory Into A Reinforcement Learning Agent Using Reservoir Sampling (2018)0.00
• Episodic Reinforcement Learning With Expanded State-reward Space (2024)0.00
• Breaking The Sample Complexity Barrier To Regret-optimal Model-free Reinforcement Learning (2021)0.00
• Measuring Progress In Deep Reinforcement Learning Sample Efficiency (2021)0.00