Interval Timing In Deep Reinforcement Learning Agents

Abstract

The measurement of time is central to intelligent behavior. We know that both animals and artificial agents can successfully use temporal dependencies to select actions. In artificial agents, little work has directly addressed (1) which architectural components are necessary for successful development of this ability, (2) how this timing ability comes to be represented in the units and actions of the agent, and (3) whether the resulting behavior of the system converges on solutions similar to those of biology. Here we studied interval timing abilities in deep reinforcement learning agents trained end-to-end on an interval reproduction paradigm inspired by experimental literature on mechanisms of timing. We characterize the strategies developed by recurrent and feedforward agents, which both succeed at temporal reproduction using distinct mechanisms, some of which bear specific and intriguing similarities to biological systems. These findings advance our understanding of how agents come

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keydeverett2019interval

Related papers

• Emergent Time-keeping Mechanisms In A Deep Reinforcement Learning Agent Performing An Interval Timing Task (2025)0.00
• Learning When To Act: Interval-aware Reinforcement Learning With Predictive Temporal Structure (2026)0.00
• Deep Reinforcement Learning With Time-scale Invariant Memory (2024)0.00
• Time Limits In Reinforcement Learning (2017)0.00
• Modulation Of Temporal Decision-making In A Deep Reinforcement Learning Agent Under The Dual-task Paradigm (2025)0.00
• Tempo Adaptation In Non-stationary Reinforcement Learning (2023)0.00
• Emergence Of Adaptive Circadian Rhythms In Deep Reinforcement Learning (2023)0.00
• Time-aware Q-networks: Resolving Temporal Irregularity For Deep Reinforcement Learning (2021)0.00