AMESA Simulation API

You can integrate your simulator with the AMESA SDK by using the ServerAMESA class. This class provides the necessary methods for the simulator to interact with the AMESA SDK.

The methods of the ServerAMESA class allow the AMESA SDK to automatically take care of serializing and deserializing the different requests and responses.

To conform your simulator to the AMESA SDK, you must define a server implementation class that defines methods of how to talk with the AMESA SDK.

AMESA’s simulation API extends the the gymnasium.Env standards.

Set Up the Simulation Environment Instance

Make

Make is a request to create a new instance of the environment with the specifications requested.

• string env_Id; Identifier for the type of environment to create.

• dictionary env_Init; Initial configuration for the environment, as defined within the runtime configuration (link to section about how to define runtime configuration parameters)

  async def make(self, env_id: str, env_init: dict):
        self.env_id = env_id if env_id else self.env_id
        self.env_init = env_init if env_init else self.env_init

        self.env = Sim(self.env_init)

        return {
            "id": "my_simulator",
        }

Sensor_space_info

Sensor_space_info provides details about the environment’s sensor space.

async def sensor_space_info(self) -> gym.Space:
        return self.env.sensor_space

Action_space_info

Action_space_info defines the agent system's action space.

async def action_space_info(self) -> gym.Space:

Action_space_sample

The action_space_sample function returns an element of the simulator’s action space.

   async def action_space_sample(self):
        return self.env.action_space.sample()

Run the Simulation Environment Instance

Reset

Reset is a request to reset the environment, and returns the first observation of the newly reset environment.

• observation Initial observation of the environment.

• Dictionary info Additional information about the reset environment.

  async def reset(self): 
        return self.env.reset()

Step

Step provides the agent system action to be applied to the environment. The return structure is as follows:

• observation; The observation following the action.

• float reward The reward received after taking the action.

• bool terminated Whether the episode has ended.

• bool truncated Whether the episode was truncated before a natural conclusion.

• Dictionary info Additional information about the step.

async def step(self, action):
        return self.env.step(action)

Close

Close denotes the simulator is done being used and may perform any necessary cleanups required.

async def close(self):
        self.env.close()

Set_Scenario

Set_scenario tells the simulator the current scenario the agent system wishes to train on. Learn more about scenarios.

  async def set_scenario(self, scenario):
        self.env.scenario = scenario

Get_Scenario

Get_scenario returns the scenario that the simulation is currently running.

async def get_scenario(self):
        if self.env.scenario is None:
            return None

        return self.env.scenario

Create Visualizations

Get_Render

Get_render provides the current rendered image of the environment, either as a numpy array or a string.

 async def get_render(self):
        return self.env.render()