🏋️‍♀️ bevy_rl

Reinforcement Learning for Bevy Engine

Build Reinforcement Learning Gym environments with Bevy engine to train AI agents that can learn from screen pixels or defined obeservation state.

Compatibility

| bevy version | bevy_rl version | | ------------ | :-------------: | | 0.7 | 0.0.5 | | 0.8 | 0.8.4 | | 0.9 | 0.9.8-beta | | 0.10 | 0.10.0 |

📝Features

Set of APIs to implement OpenAI Gym interface, such as reset, step, render and associated simulator states
Multi-Agent support
Rendering screen pixels to RAM buffer — for training agents with raw pixels
REST API to control agents

👩‍💻 Usage

1. Define Action and Obeservation Space

Observation space needs to be Serializable for REST API to work.

```rust // Action space

[derive(Default)]

pub struct Actions { // actuator_signals: [f32; 3], }

// Observation space

[derive(Default, Serialize, Clone)]

pub struct Observations { // agent_coords: [(f32, f32); 16], } ```

2. Enable AI Gym Plugin

Width and height should exceed 256, otherwise wgpu will panic.

rust // Setup bevy_rl let ai_gym_state = AIGymState::<Actions, State>::new(AIGymSettings { width: u32, // Width and height of the screen height: u32, // ... num_agents: 1, // Number of agents — each will get a camera handle render_to_buffer: false, // You can disable rendering to buffer pause_interval: 0.01, // 100 Hz ..default() }); app.insert_resource(ai_gym_state) .add_plugin(AIGymPlugin::<Actions, Observations>::default());

2.1 (Optional) Enable Rendering to Buffer

If your environment wants to export raw pixels, you will need to attach a render target to each camera you want to export them from. Render targets are copied each frame from GPU memory to RAM buffers so that they can be accessed with REST API.

```rust pub(crate) fn spawncameras( aigymstate: Res>, ) { let mut aigymstate = aigymstate.lock().unwrap(); let aigymsettings = aigym_state.settings.clone();

for i in 0..ai_gym_settings.num_agents {
    let render_image_handle = ai_gym_state.render_image_handles[i as usize].clone();
    let render_target = RenderTarget::Image(render_image_handle);
    let camera_bundle = Camera3dBundle {
        camera: Camera {
            target: render_target,  // Render target is baked in bevy_rl and used to export pixels
            priority: -1,           // set to -1 to render at the firstmost pass
            ..default()
        },
        ..default()
    };
    commands.spawn(camera_bundle);
}

} ```

4. Handle bevy_rl events

bevy_rl will communicate with your environment through events. Those events initiate from REST API or bevy_rl.SimulationPauseTimer that pauses the simulation with given interval (AIGymSettings.pause_interval).

| Event | Description | Usage | | -------------- | ---------------------------------- | ------------------------------------------------------------------------------------------ | | EventReset | Reset environment to initial state | You should rebuild your evnironment here | | EventControl | Switch to control state | You should recieve actions here and apply them to your environment (and resume simulation) | | EventPause | Pause environment execution | Pause physics engine or game clock and take snapshot of your game state |

Here's example of how to handle those events:

```rust // EventPauseResume fn bevyrlpauserequest( mut pauseeventreader: EventReader, aigymstate: Res>, ) { for _ in pauseeventreader.iter() { // Pause simulation (physics engine) // ... // Collect state into serializable struct let envstate = Observations(...); // Set bevyrl gym state let mut aigymstate = aigymstate.lock().unwrap(); aigymstate.setenvstate(envstate); } }

// EventControl fn bevyrlcontrolrequest( mut pauseeventreader: EventReader, mut simulationstate: ResMut>, ) { for control in pauseeventreader.iter() { let unparsedactions = &control.0; for i in 0..unparsedactions.len() { if let Some(unparsedaction) = unparsedactions[i].clone() { let action: Vec = serdejson::fromstr(&unparsedaction).unwrap(); // Pass control inputs to your agents // ... } } // Resume simulation (physics engine) // ... // Return to running state; note that it uses pop/push to avoid // entering SystemSet::on_enter(SimulationState::Running) which initialized game world anew simulationstate.pop().unwrap(); } }

/// Handle bevyrl::EventReset pub(crate) fn bevyrlresetrequest( mut reseteventreader: EventReader, mut commands: Commands, mut walls: Query, mut players: Query<(Entity, &Actor)>, mut simulationstate: ResMut>, aigymstate: Res>, ) { if resetevent_reader.iter().count() == 0 { return; }

// Reset envrionment state here

// Return simulation in Running state
simulation_state.set(SimulationState::Running).unwrap();

// tell bevy_rl that environment is reset and return response to REST API
let ai_gym_state = ai_gym_state.lock().unwrap();
ai_gym_state.send_reset_result(true);

} ```

rust app.add_system_set( SystemSet::on_update(SimulationState::PausedForControl) .with_system(bevy_rl_control_request) .with_system(bevy_rl_reset_request) .with_system(bevy_rl_pause_request), );

💻 AIGymState API

Those methods are available on AIGymState resource. You should use them to alter bevy_rl internal state.

| Method | Description | Usage | | -------------------------------------------------- | ----------------------------------- | -------------------------------------------------------------------------------------------- | | set_reward(agent_index: usize, score: f32) | Set reward for an agent | When a certain event happens, you can set reward for an agent. | | set_terminated(agent_index: usize, result: bool) | Set termination status for an agent | Once your agent is killed, you should set it's status to true. Useful for Multi-agent. | | reset() | Reset bevy_rl state | You should call this method when you reset your environment to clear exported state history | | set_env_state(state: State) | Set current environment state | When you serialize your environment state, you should set it here. | | send_reset_result(result: bool) | Send reset result to REST API | You should call this method when you have reset your environment to sychronize with REST API |

🌐 REST API

Accessing bevy_rl-enabled environment is possible through REST API. Here's a list of available endpoints:

| Method | Verb | bevy_rl version | | ----------------- | ------- | ------------------------------------------- | | Camera Pixels | GET | http://localhost:7878/visual_observations | | State | GET | http://localhost:7878/state | | Reset Environment | GET | http://localhost:7878/reset | | Step | GET | http://localhost:7878/step?payload=ACTION |

bevyrlshooter implements an example Python wrapper.

✍️ Examples

bevyrlshooter — example FPS project
bevyquadrupedneuralcontrol — quadruped locomotion with bevymujoco and bevy_rl