rogerscristo / GYM_XPLANE_ML

GYM Environment for XPlane. Reinforcement Learning and Autonomous Piloting.

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XPlane_ML_Reinforcement_Learning_Autopilot

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This project documents set up of Reinforcement learning environment for flight control. This will enable each user/learner/student/flight enthusiast to come with their own RL algorithm that can interact with and control in real time an aircraft in a simulation environment. The simulator used is X-Plane (the flight environment). This gives a realistic environment to work with together with parameters to keep the simulation as close to reality as possible.

Update: Use the leaderboard wiki Page--> (Leaderboard) to add details of your own scenario or algorithm.

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Agent-Environment Interaction Flow

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Example Training Episode

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Action Space Parameters

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The action space parameters. Select from the list the appropriate parameters for any task or scenario . For example one might decide that the first 4 parameters are important for a defined task.

Action Space Parameter	Action type	Action Value Range
Latitudinal Stick	Box	[-1,1]
Longitudinal Stick	Box	[-1,1]
Rudder Pedals	Box	[-1,1]
Throttle	Box	[-1/4,1]
Gear	Discrete	0,1
Flaps	Box	[0,1]
Speedbrakes	Box	[-0.5,1.5]

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State Space Parameters

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The state space parameters are well documented in XPlane Data Ref . The number of state space parameters will depend on the task. It is possible to use a derived state parameter. Usually a UDP connection (XPlaneConnect) is required to read this parameter from XPlane. An example of how to read parameter from UDP connection is shown below:

client = xpc.XPlaneConnect() # UDP connector
client.getDREF("sim/flightmodel/position/P")[0][0] # moment P
client.getPOSI() # get the lat, long,altitude, pitch, roll, heading, gear

A typical state space parameter configuration is shown below:

State Space Parameter	State type	State Value Range
velocity_x	Box	[0,120]
velocity_y	Box	[0,120]
delta_heading	Box	[-300,300]

The range of each parameter value would also depend on the configuration.

client (UDP connector) already has a function getPOSI that helps read the latitude, longitude, altitude, pitch, roll, heading. Other parameters could be added by using the client getDREF function. Note that the string "sim/flightmodel/position/P" is gotten from the Xplane Dataref referenced earlier (XPlane Data Ref)

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Flight Dashboard

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To learn more about this go to : FlightDataVizDashBoard

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Requirements

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• X Plane (Demo version) (main requirement)
  • XPLane
• XPlaneConnect (replace this with different python udp connection file if required)
  • XPlaneConnect [https://github.com/nasa/XPlaneConnect]
• RL Algorithm (define your own RL algorithm or use standard baslines. )
  • Tensorforce
  • RLLIB
  • OpenAI Baselines
• Lua Programming/FlyWithLua (for seamless interaction with XPlane)

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General setup

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• GYM XPLANE ENVIRONMENT
• MULTI AGENT ENVIRONMENT SET UP (Work in progress)

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TO DO

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• Use XVFB for fake display.
• Docker support (under test)
• Algorithm implementations (high priority)
• Real Time flight and data visualization interface.