Experience Replay Using Transition Sequences.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning

Background:

• Experience replay is crucial for enhancing sample efficiency in reinforcement learning (RL).
• Current methods often replay individual transitions, limiting learning speed and value function propagation.
• Off-policy RL agents require effective strategies to leverage past experiences.

Purpose of the Study:

• To propose a novel sequence-based experience replay approach for off-policy reinforcement learning.
• To accelerate agent learning and improve value function accuracy through strategic transition replay.
• To enhance the utilization of agent-environment interactions.

Main Methods:

• Developing a method to select and replay relevant transition sequences.
• Artificially constructing novel transition sequences using historical interaction data.
• Applying the approach to modified Mountain Car and Puddle World reinforcement learning tasks.

Main Results:

• Empirically demonstrated faster learning of value functions compared to standard experience replay.
• Achieved more accurate value function estimation.
• Showcased the effectiveness of sequence replay in propagating value information across the state-action space.

Conclusions:

• The proposed sequence replay method significantly improves learning speed and accuracy in off-policy RL.
• This approach offers a more efficient way to utilize agent experience.
• Potential extensions and applications in complex RL environments warrant further investigation.