A Reinforcement Learning Approach for Control of a Nature-Inspired Aerial Vehicle
Explore this paper's citation graph
- Type
- article
- Published
- 2019-05-01
- Cited by
- 12
- References
- 16
- OpenAlex
- https://openalex.org/W2967419209
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:199541547
Keywords
Reinforcement learning, Porting, Computer science, PID controller, Underactuation
References
- The guided samara : design and development of a controllable single-bladed autorotating vehicle
- Mathematical Model of a Monocopter Based on Unsteady Blade-Element Momentum Theory
- Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search
- On the Theory of the Brownian Motion
- Pitch and Heave Control of Robotic Samara Micro Air Vehicles
- Human-level control through deep reinforcement learning
- A reinforcement learning approach towards autonomous suspended load manipulation using aerial robots
- Deterministic Policy Gradient Algorithms
- Design and implementation of an unmanned tail-sitter
- Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
- Control of a Quadrotor With Reinforcement Learning
- Design and dynamic analysis of a Transformable Hovering Rotorcraft (THOR)
- RLlib: Abstractions for Distributed Reinforcement Learning
- Distributed Prioritized Experience Replay
- Continuous control with deep reinforcement learning
- Continuous control with deep reinforcement learning
- RLlib: Abstractions for Distributed Reinforcement Learning
Cited by
- High Angular Rates Estimation using Numerical Phase-Locked Loop Method
- A Central Pattern Generator-Based Control Strategy of a Nature-Inspired Unmanned Aerial Vehicle
- Flydar: Magnetometer-based High Angular Rate Estimation during Gyro Saturation for SLAM
- Joint Mechanical Design and Flight Control Optimization of a Nature-Inspired Unmanned Aerial Vehicle via Collaborative Co-Evolution
- Deep Reinforcement Learning Approach for Flocking Control of Multi-agents
- Deep Reinforcement Learning With NMPC Assistance Nash Switching for Urban Autonomous Driving
- Carrier Aircraft Landing Control Technology Based on Deep Reinforcement Learning
- Deterministic Framework based Structured Learning for Quadrotors
- Residual dynamics learning for trajectory tracking for multi-rotor aerial vehicles
- Reinforcement Learning-based Data-driven Control Design for Motion Control Systems
- HM-DRL: Enhancing multi-agent pathfinding with a heatmap-based heuristic for distributed deep reinforcement learning
- Deep Deterministic Policy Gradient with Symmetric Data Augmentation for Lateral Attitude Tracking Control of a Fixed-wing Aircraft
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