Top 4 deep q-network paper in 2022

Author: arxiv.org

Date Submitted: 01/26/2021 09:52 PM

Average star voting: 5 ⭐ ( 46763 reviews)

Summary: In my previous post A First Look at Reinforcement Learning, I attempted to use Deep Q learning to solve the CartPole problem. In this post, I will be further exploring Deep Q learning but in the…

Match with the search results: We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement ……. read more

Author: www.cs.toronto.edu

Date Submitted: 08/04/2019 08:04 PM

Average star voting: 4 ⭐ ( 21074 reviews)

Summary: This is the second post devoted to Deep Q-Network (DQN), in the “Deep Reinforcement Learning Explained” series, in which we will analyse some challenges that appear when we apply Deep Learning to…

Match with the search results: This paper demonstrates that a convolutional neural network can overcome these challenges to learn successful control policies from raw video data in ……. read more

Author: www.nature.com

Date Submitted: 05/24/2021 12:21 AM

Average star voting: 5 ⭐ ( 34697 reviews)

Summary: A deep Q network (DQN) (Mnih et al., 2013) is an extension of Q learning, which is a typical deep reinforcement learning method. In DQN, a Q function expresses all action values under all states, and it is approximated using a convolutional neural network. Using the approximated Q function, an optimal policy can be derived. In DQN, a target network, which calculates a target value and is updated by the Q function at regular intervals, is introduced to stabilize the learning process. A less frequent updates of the target network would result in a more stable learning process. However, because the target value is not propagated unless the target network is updated, DQN usually requires a large number of samples. In this study, we propose Constrained DQN that uses the difference between the outputs of the Q function and the target network as a constraint on the target value. Constrained DQN updates parameters conservatively when the difference between the outputs of the Q function and the target network is large, and it updates them aggressively when this difference is small. In the proposed method, as learning progresses, the number of times that the constraints are activated decreases. Consequently, the update method gradually approaches conventional Q learning. We found that Constrained DQN converges with a smaller training dataset than in the case of DQN and that it is robust against changes in the update frequency of the target network and settings of a certain parameter of the optimizer. Although Constrained DQN alone does not show better performance in comparison to integrated approaches nor distributed methods, experimental results show that Constrained DQN can be used as an additional component to those methods.

Match with the search results: To achieve this, we developed a novel agent, a deep Q-network (DQN), which is able to combine reinforcement learning with a class of artificial ……. read more

Author: paperswithcode.com

Date Submitted: 02/12/2021 10:25 PM

Average star voting: 4 ⭐ ( 87065 reviews)

Summary:

Match with the search results: A DQN, or Deep Q-Network, approximates a state-value function in a Q-Learning framework with a neural network. In the Atari Games case, they take in several ……. read more