Quantum deep Q learning with distributed prioritized experience replay

2023-04-19 · Samuel Yen-Chi Chen

This paper introduces the QDQN-DPER framework to enhance the efficiency of quantum reinforcement learning (QRL) in solving sequential decision tasks. The framework incorporates prioritized experience replay and asynchronous training into the training algorithm to reduce the high sampling complexities. Numerical simulations demonstrate that QDQN-DPER outperforms the baseline distributed quantum Q learning with the same model architecture. The proposed framework holds potential for more complex tasks while maintaining training efficiency.

📄 PDF Abstract BibTeX arXiv:2304.09648

Code (0)

등록된 구현이 없습니다.

Tasks

Q-Learningreinforcement-learning Reinforcement Learning

Methods 이 논문이 사용한 방법론

Prioritized Experience Replay Prioritized Experience Replay is a type of experience replay in reinforcement learning where we more frequently replay…

Experience Replay Experience Replay is a replay memory technique used in reinforcement learning where we store the agent’s experiences at each time-step, $e\_{t} = \left(s\_{t}, a\_{t}, r\_{t},…

Distributed Prioritized Experience Replay

2018-03-02 · ICLR 2018 1 · Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron 외

We propose a distributed architecture for deep reinforcement learning at scale, that enables agents to learn effectively from orders of magnitude more data than previously possible. The algorithm decouples acting from le…

Atari Games Deep Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Double Prioritized State Recycled Experience Replay

2020-07-08 · Fanchen Bu, Dong Eui Chang

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 a…

Atari Games

Recurrent Experience Replay in Distributed Reinforcement Learning

2019-05-01 · ICLR 2019 5 · Steven Kapturowski, Georg Ostrovski, Will Dabney, John Quan 외

Building on the recent successes of distributed training of RL agents, in this paper we investigate the training of RNN-based RL agents from distributed prioritized experience replay. We study the effects of parameter la…

Atari Games reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

A novel DDPG method with prioritized experience replay

2017-10-01 · IEEE International Conference on Systems, Man and Cybernetics (SMC) 2017 10 · Yuenan Hou, Lifeng Liu, Qing Wei, Xudong Xu 외

Recently, a state-of-the-art algorithm, called deep deterministic policy gradient (DDPG), has achieved good performance in many continuous control tasks in the MuJoCo simulator. To further improve the efficiency of the e…

continuous-control Continuous Control MuJoCo OpenAI Gym

Prioritized Experience Replay

2015-11-18 · Tom Schaul, John Quan, Ioannis Antonoglou, David Silver

Experience replay lets online reinforcement learning agents remember and reuse experiences from the past. In prior work, experience transitions were uniformly sampled from a replay memory. However, this approach simply r…