Exploration for Countering the Episodic Memory

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computational Neuroscience

Background:

• Reinforcement learning (RL) agents learn through trial and error.
• Effective exploration is crucial for RL agent performance, especially in complex environments.
• Traditional count-based exploration struggles with high-dimensional state spaces common in deep RL.

Purpose of the Study:

• To develop a novel exploration strategy for reinforcement learning using episodic memory.
• To enhance the agent's ability to track and learn from previously encountered states.
• To improve exploration efficiency in high-dimensional state and action spaces.

Main Methods:

• Implemented an episodic memory module to record encountered states, simulating hippocampal function.
• Utilized this memory module as a state-counting mechanism for exploration.
• Tested the approach on the OpenAI platform, comparing it against the CTS model.

Main Results:

• The episodic memory approach demonstrated superior state counting accuracy compared to the CTS model.
• The method achieved successful results when applied to high-dimensional object detection and tracking tasks.
• This indicates improved exploration capabilities in complex, real-world scenarios.

Conclusions:

• Episodic memory provides an effective mechanism for state estimation and exploration in reinforcement learning.
• The proposed method offers a promising alternative to heuristic-driven exploration in deep RL.
• This approach has practical implications for advancing AI in complex domains like object detection and tracking.