Double Prioritized State Recycled Experience Replay
| Autor: | Dong Eui Chang, Fanchen Bu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Multimedia Computer science 05 social sciences Machine Learning (stat.ML) 010501 environmental sciences computer.software_genre 01 natural sciences Machine Learning (cs.LG) Statistics - Machine Learning 0502 economics and business Reinforcement learning State (computer science) 050207 economics computer 0105 earth and related environmental sciences |
| Zdroj: | 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia). |
| Popis: | Experience replay enables online reinforcement learning agents to store and reuse the previous experiences of interacting with the environment. In the original method, the experiences are sampled and replayed uniformly at random. A prior work called prioritized experience replay was developed where experiences are prioritized, so as to replay experiences seeming to be more important more frequently. In this paper, we develop a method called double-prioritized state-recycled (DPSR) experience replay, prioritizing the experiences in both training stage and storing stage, as well as replacing the experiences in the memory with state recycling to make the best of experiences that seem to have low priorities temporarily. We used this method in Deep Q-Networks (DQN), and achieved a state-of-the-art result, outperforming the original method and prioritized experience replay on many Atari games. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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