VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.
In the current implementation, even the Agent's angle could be changed, but the sensor Lidar's ray angles are fixed relative to the world frame, which is not obvious when the lidar has 360 fov.
Since the lidar supports different fovs, it seems the current configuration is kind of anti-intuition.
In the current implementation, even the Agent's angle could be changed, but the sensor Lidar's ray angles are fixed relative to the world frame, which is not obvious when the lidar has 360 fov. Since the lidar supports different fovs, it seems the current configuration is kind of anti-intuition.