The FLUIDS Gym Environment provides a simpler common interface to the FLUIDS simulator that is compatible with general agents following the Gym API.

import gym
import gym_fluids

env = gym.make("fluids-v2")

The current Gym environment supports 1 controlled car interacting with 10 background cars and 5 pedestrians. The action space is a [steering, acc] pair. The observation space is a 500 x 500 RGB image from the car’s perspective. A reward function considering collisions and distance traveled along the given trajectory is provided.

Supervisor Agent

FLUIDS packages a supervisor agent that interfaces with FLUIDS’s multiagent planner. The supervisor is useful for collecting data for and benchmarking imitation learning algorithms. To use the supervisor, pass the observation and info dictionary to the fluids_supervisor object. Since FLUIDS’s multiagent planner requires full information of the world, the observation itself is not sufficient.

obs, rew, done, info = env.step(action) action = gym_fluids.agents.fluids_supervisor(obs, info)