Recovering Permuted Sequential Features for effective Reinforcement Learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Real-world visual Reinforcement Learning (RL) faces challenges with sample inefficiency and limited generalization.
• Existing methods often focus on semantic information, neglecting spatial aspects and task-relevant variables for generalization.

Purpose of the Study:

• To enhance sample efficiency and generalization in visual RL tasks.
• To introduce a novel auxiliary task that learns both semantic and spatial information.

Main Methods:

• Propose Recovering Permuted Sequential Features (RPSF) as an auxiliary task.
• RPSF learns spatial structure by recovering permuted feature sequences, improving holistic representations.
• The method is compatible with Convolutional Neural Networks (CNNs) and Transformer architectures.

Main Results:

• RPSF significantly improves sample efficiency and generalization compared to baseline RL algorithms.
• The method demonstrates superior performance across diverse tasks in unseen environments.
• Enhanced representations mitigate the impact of task-relevant and task-irrelevant variable changes.

Conclusions:

• Recovering Permuted Sequential Features (RPSF) effectively addresses key limitations in visual RL.
• The proposed method offers a robust approach for learning more generalizable and data-efficient policies.
• RPSF shows promise for advancing RL applications in complex visual domains.