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Showing 1 to 12 of 12 for “"DDPG"”.
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Deep Reinforcement Learning for Robotic Tasks: Manipulation and Sensor Odometry
… GA to Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) (GA+DDPG+HER). Finally, we kept researching DDPG and created an algorithm named AACHER. AACHER uses HER and many independent instances of actors and critics from the DDPG to increase a robot's learning …
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Reinforcement Learning Control for Mobile Robot Parking with Safety Constraints
… We apply Deep Deterministic Policy Gradient (DDPG) methods for continuous control and evaluate policies across three Simulink environments of increasing fidelity: a kinematic model, a dynamic model, and a dynamic model with actuator disturbance. In parking tasks, DDPG learns smooth, stable …
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Mechanical Design and Learned Control System Development of Fiber Extrusion Device on Industrial Programmable Logic Controller (PLC) Platform.
… (PLC) based on machine learning models such as DDPG (Deep Deterministic Policy Gradient). To develop and train such control algorithms, a desktop version of a fiber draw tower was designed, manufactured, and controlled via a PLC. System dynamics data was collected using a readily available …
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Artificial Neural Network-Based Robotic Control
… using the deep deterministic policy gradients (DDPG) algorithm, an actor-critic reinforcement learning strategy, originally conceived by Google DeepMind. After training, the robot performs controlled locomotion within an enclosed area. The paper also details the robot design process and explores …
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Deep Reinforcement Learning-Based Approaches for the MPPT Control of Standalone Solar PV Systems
… such as Deep Deterministic Policy Gradient (DDPG), Soft Actor-Critic (SAC), and Deep Q-Network (DQN) algorithm. They are utilized mainly due to their robustness and ability to handle continuous state spaces, unlike the traditional RL method, which operates with discrete action and state …
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Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks
… and the Deep Deterministic Policy Gradient (DDPG) are compared for robust control when the trailer is changed. This investigation quantifies the capabilities and limitations of both controllers in simulation using a kinematic model. The controllers are evaluated for generalization by altering …
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Resource Allocation in 5G: NR Sidelink Mode 2 & Wi-Fi 6/7
… to employ Deep Deterministic Policy Gradient (DDPG) algorithm to overcome the impact of inter-vehicle collaboration in the platoon based on local information. A Monte Carlo simulator is then used to verify the analytical models' results. The numerical results show that IRS significantly …
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Development and Deployment of a Dynamic Soaring Capable UAV using Reinforcement Learning
… of the UAV. Deep deterministic policy gradient (DDPG), an actor-critic RL algorithm, was used to train a closed-loop Path Following (PF) agent and an Unguided Energy- Seeking (UES) agent. Several generations of the PF agent were presented, with the final generation capable of controlling the …
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Cognitive GPR for subsurface sensing based on edge computing and deep reinforcement learning
… called deep deterministic policy gradient (DDPG) with a new reward function derived from 3D GPR data. The proposed methods are evaluated using GPR modeling and simulation software called GprMax. Simulation results show that our proposed cognitive GPRs outperform other GPR systems in terms of …
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Ml Controllers With Memory For Robust Quadrotor Control And Research
… reinforcement learning methods, such as PPO or DDPG learning. These learned controllers require many action-time steps, and performing many of these actions directly while learning can result in undesirable effects on the real system. Undesirable effects may include hardware damage, behaving …
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Using Reinforcement Learning and Bayesian Optimization on Problems in Vehicle Dynamics and Random Vibration Environmental Testing
… (PILCO) and Deep Deterministic Policy Gradient (DDPG). PILCO was used to demonstrate the need of incorporating wheel-slip and the need for a neural network approach to capture all regions of the vehicle dynamic behavior. Reward functions were designed to incentivize the RL algorithms to achieve …
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Adaptive neuro-fuzzy inference system based neural network and parameter constraints
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms