arXiv:2410.04498v2 Announce Type: replace Abstract: In sparse reward scenarios of reinforcement learning (RL), the memory mechanism provides promising shortcuts to policy optimization by reflecting on past experiences like humans. However, current memory-based RL methods simply store and reuse high-value policies, lacking a deeper refining and filtering of diverse past experiences and hence limiting the capability of memory. In this paper, we propose AdaMemento, an adaptive memory-enhanced RL framework. Instead of just memorizing positive past experiences, we design a memory-reflection module
The continuous evolution of AI research seeks more efficient and robust learning mechanisms, particularly for sparse reward environments which are prevalent in complex real-world applications.
Adaptive memory mechanisms like AdaMemento could significantly improve the sample efficiency and performance of reinforcement learning agents, making AI applicable to a wider range of challenging problems.
This advancement proposes a new approach to memory utilization in RL by not just storing positive experiences but actively refining and filtering diverse past data, potentially leading to more sophisticated and faster learning agents.
- · AI researchers
- · Robotics developers
- · Developers of autonomous systems
- · Traditional RL methods with naive memory
- · Systems requiring extensive pre-training data
Reinforcement learning agents will become more effective in complex environments with sparse rewards.
This improved efficiency could accelerate the development and deployment of advanced autonomous AI agents across various industries.
The enhanced capabilities of AI agents might drive further consolidation and automation in sectors currently reliant on human decision-making and intricate control.
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Read at arXiv cs.LG