QuadSim: A Quadcopter Rotational Dynamics Simulation Framework For Reinforcement Learning Algorithms
This provides a tool for researchers in robotics and reinforcement learning to simulate quadcopter dynamics, but it is incremental as it builds on existing simulation and RL frameworks.
The study developed a quadcopter rotational dynamics simulation framework to test reinforcement learning algorithms, enabling deterministic and stochastic simulations with compatibility to OpenAI Gym and multiprocessing, and trained state-of-the-art deep RL algorithms for comparison.
This study focuses on designing and developing a mathematically based quadcopter rotational dynamics simulation framework for testing reinforcement learning (RL) algorithms in many flexible configurations. The design of the simulation framework aims to simulate both linear and nonlinear representations of a quadcopter by solving initial value problems for ordinary differential equation (ODE) systems. In addition, the simulation environment is capable of making the simulation deterministic/stochastic by adding random Gaussian noise in the forms of process and measurement noises. In order to ensure that the scope of this simulation environment is not limited only with our own RL algorithms, the simulation environment has been expanded to be compatible with the OpenAI Gym toolkit. The framework also supports multiprocessing capabilities to run simulation environments simultaneously in parallel. To test these capabilities, many state-of-the-art deep RL algorithms were trained in this simulation framework and the results were compared in detail.