Implemented, but not thoroughly tested and hence not considered in this paper Implemented using the Bullet and Psyonix impulses, but not thoroughly tested Which may differ to the approach taken in Rocket League that remains unclear. Stiffness of front wheels: 163.9 1 s 2 and of back wheels: 275.4 1 s 2ĭamper front and back is set to 30 1 s. Raise sticky forces for wall stabilization to an acceleration of 500 uu s 2 The stabilization torque is denoted by an acceleration of 50 rad s 2. Within the bounce’s computation a ball radius of 91.25uu is considered.Ī drag of − 525 uu s 2 is used, which is reduced by more than half when the car is upside down. On the center of the ball, which allows a better prediction and control of collisions. The impulse by theīullet engine replaces the Unity one. Used for the ball-to-car interaction and car-to-car interaction. angular velocity during dodge from 5.5 rad s to 7.3 rad sĪdjust drag coefficients for roll to − 4.75 and pitch to − 2.85 Radius of the ball is set to 93.15uu (value in Rocket League 92.75uu) Iii-a Implementation of the Training Simulation Physics ComponentĪdditional Information and Different ParametersĬar model Octane and its collision mesh is used The code is open source 2 2 2Link to Github. Provides the interface to Rocket League where the training situations can be reproduced.Īfterward, the DRL environments, designated for training, and their properties are detailed. This section starts out by providing an overview of vital components of Rocket League’s physical gameplay mechanics, which are implemented in the training simulation based on the game engine Unity and the ML-Agents Toolkit. Overall, it has been found that the respective policies learned with simulations execute more successfully on real robots when GraspGAN is used. The synthesized pseudo-real images correct the sim-to-real gap to some extent. GraspGAN provides a method called pixel-level domain adaptation, which translates synthetic images to realistic ones at the pixel level. This property can be used for image synthesis to model the transformation between simulated and real images. GANs are able to generate synthetic data with good generalization ability. Which utilizes a generative adversarial network (GAN). The translation of synthetic images to realistic ones at the pixel level is employed by a method called GraspGAN To address the inability to exactly match the real-world environment, a challenge commonly known as sim-to-real gap, steps have also been taken towards generalized sim-to-real transfer for robot learning. Popular applications for sim-to-real transfer in robotics have been autonomous racing, Robot Soccer, navigation, and control tasks. Sim-to-real transfer has been predominantly applied to RL-based robotics where the robotic agent has been trained with state-of-the-art RL techniques like PPO. It allows the transition of an RL agent’s behavior, which has been trained in simulations, to real-world environments. In general, sim-to-real transfer is a well-established method for robot learning and is widely used 1: The game of Rocket League (top) and the contributed simulation (bottom), which notably advances its ancestor project RoboLeague. Therefore it makes sense to look for alternative ways to tackle difficult problems.įig. Video games that suffer from not being able to be sped up significantly, risk minimal running times and hence repeatability. Moreover, not many research groups have such resources at their disposal. However, this way is closed for games that run only on specific platforms and are thus very hard to parallelize. OpenAI Five for DotA 2 is an example of the utilization of hundreds of thousands of computing cores in order to achieve high throughput in terms of played games. To deal with this problem, fast running environments or high amounts of computing resources are vital. Unfortunately, and despite many improvements achieved in AI in recent years, the utilized Deep Learning methods are still relatively sample inefficient. , have been possible only because the employed algorithms were able to train on huge numbers of games on the order of billions or more. The spectacular successes of agents playing considerably difficult games, such as and DotA 2
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