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Self-Paced Prioritized Curriculum Learning With Coverage Penalty in Deep Reinforcement Learning
Summary
A new deep reinforcement learning training method, Deep Curriculum Reinforcement Learning (DCRL), improves sample efficiency and diversity. DCRL outperforms existing methods like DQN and PER on Atari games.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Deep Learning
Background:
- Deep reinforcement learning (DRL) algorithms often struggle with sample efficiency and training stability.
- Experience replay is a common technique to improve DRL, but selecting relevant data remains a challenge.
Purpose of the Study:
- To introduce a novel training paradigm, Deep Curriculum Reinforcement Learning (DCRL), for enhanced DRL.
- To improve sample efficiency and diversity in DRL by adaptively selecting transitions from replay memory.
Main Methods:
- DCRL utilizes a self-paced prioritized curriculum learning approach with a coverage penalty.
- Transition complexity is determined by self-paced priority (temporal-difference error and curriculum difficulty) and coverage penalty (sample diversity).
Main Results:
- DCRL demonstrated superior performance compared to Deep Q Network (DQN) and Prioritized Experience Replay (PER) on Atari 2600 games.
- The DCRL training paradigm proved effective when applied to other DRL methods like Double DQN and Dueling Network.
Conclusions:
- DCRL offers improved training efficiency and robustness for deep reinforcement learning.
- The proposed curriculum learning approach enhances the performance of memory-based DRL algorithms.
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