reinforcement learning, deep Q-network, double DQN, dueling DQN, prioritized experience replay Topicsreinforcement-learning neural-network cpp gpu atari-games ResourcesReadme LicenseApache-2.0 license Activity
reinforcement-learninggaedeep-reinforcement-learningrainbowpython3pytorchdeep-q-networkactor-criticdouble-dqnquantile-regressiondueling-dqncategorical-dqnppoadvantage-actor-critica2cdeep-recurrent-q-networkprioritized-experience-replaymulti-step-learningnoisy-networksdeeprl-tutorials ...
Value function for the state-action pair for double Q-learning having the parameters ͝θ͝ ͝qθ͝− Target state and action pair to compute the TD error for double Q-learning having parameters ͝θ͝− vη Value stream in dueling network a˜ψ The advantage stream in the ...
This study proposes a MASS autonomous navigation system using dueling deep Q networks prioritized replay (Dueling-DQNPR) based on the ship automatic identification system (AIS) big data. A navigation environment with three difficulty levels were established to train the Dueling-DQNPR network in ...
Deep Reinforcement Learning with Double Q-learning[arxiv][code] Dueling Network Architectures for Deep Reinforcement Learning[arxiv][code] Prioritized Experience Replay[arxiv][code] Noisy Networks for Exploration[arxiv][code] A Distributional Perspective on Reinforcement Learning[arxiv][code] ...
In response to these challenges, this paper presents a novel algorithm called PER-n2D3QN, which integrates prioritized experience replay, a noisy network with factorized Gaussian noise, n-step learning, and a dueling structure into a double deep Q-network. This combination enhances the efficiency ...
This paper proposes an Improved Dueling Deep Double-Q Network Based on Prioritized Experience Replay (IPD3QN) to address the slow and unstable convergence of traditional Deep Q Network (DQN) algorithms in autonomous path planning of USV. Firstly, we use the deep double Q-Network to decouple the...