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Relay hindsight experience replay: Self-guided continual reinforcement learning for sequential object manipulation tasks with sparse rewards
Learning with sparse rewards remains a challenging problem in reinforcement learning
(RL). In particular, for sequential object manipulation tasks, the RL agent generally only …
(RL). In particular, for sequential object manipulation tasks, the RL agent generally only …
Reinforcement learning control of hydraulic servo system based on TD3 algorithm
X Yuan, Y Wang, R Zhang, Q Gao, Z Zhou, R Zhou… - Machines, 2022 - mdpi.com
This paper aims at the characteristics of nonlinear, time-varying and parameter coupling in a
hydraulic servo system. An intelligent control method is designed that uses self-learning …
hydraulic servo system. An intelligent control method is designed that uses self-learning …
Addressing hindsight bias in multigoal reinforcement learning
Multigoal reinforcement learning (RL) extends the typical RL with goal-conditional value
functions and policies. One efficient multigoal RL algorithm is the hindsight experience …
functions and policies. One efficient multigoal RL algorithm is the hindsight experience …
Regularly updated deterministic policy gradient algorithm
Abstract Deep Deterministic Policy Gradient (DDPG) algorithm is one of the most well-known
reinforcement learning methods. However, this method is inefficient and unstable in practical …
reinforcement learning methods. However, this method is inefficient and unstable in practical …
Variational dynamic for self-supervised exploration in deep reinforcement learning
Efficient exploration remains a challenging problem in reinforcement learning, especially for
tasks where extrinsic rewards from environments are sparse or even totally disregarded …
tasks where extrinsic rewards from environments are sparse or even totally disregarded …
An Intelligent Strategy Decision Method for Collaborative Jamming Based On Hierarchical Multi-Agent Reinforcement Learning
W Zhang, T Zhao, Z Zhao, Y Wang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Aiming at the problem of intelligent cooperative jamming decision-making against frequency
agility and frequency diversity in cognitive electronic warfare, an intelligent cooperative …
agility and frequency diversity in cognitive electronic warfare, an intelligent cooperative …
Anticipatory Classifier System With Episode-Based Experience Replay
Deep reinforcement learning with Experience Replay (ER), including Deep Q-Network
(DQN), has been used to solve many multi-step learning problems. However, in practice …
(DQN), has been used to solve many multi-step learning problems. However, in practice …
Long-Term Feature Extraction Via Frequency Prediction for Efficient Reinforcement Learning
Sample efficiency remains a key challenge for the deployment of deep reinforcement
learning (RL) in real-world scenarios. A common approach is to learn efficient …
learning (RL) in real-world scenarios. A common approach is to learn efficient …
Prioritized hindsight with dual buffer for meta-reinforcement learning
Sharing prior knowledge across multiple robotic manipulation tasks is a challenging
research topic. Although the state-of-the-art deep reinforcement learning (DRL) algorithms …
research topic. Although the state-of-the-art deep reinforcement learning (DRL) algorithms …
Anchor: The achieved goal to replace the subgoal for hierarchical reinforcement learning
Hierarchical reinforcement learning (HRL) extends traditional reinforcement learning
methods to complex tasks, such as the continuous control task with long horizon. As an …
methods to complex tasks, such as the continuous control task with long horizon. As an …