[codex] Add networked dynamic PETC controller wrapper

docs/networked_control.md

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+# Networked Controller Wrapper
+
+This module adds a controller wrapper for sampled, delayed, bandwidth-limited
+communication. It does not replace LQR, MPC, PID, or learning controllers. It
+wraps them and changes which observation reaches the controller at each step.
+
+## Theory Mapping
+
+- `max_delay_steps` maps to the maximum allowable delay number in sampling.
+- `DelayChannel.queue_depth` maps to the number of transmitted samples waiting
+  for delivery.
+- `sampled_signal` is the source-side sampled signal.
+- `held_signal` is the destination-side signal used by the wrapped controller.
+- `update_error = held_signal - sampled_signal`.
+- `DynamicPETCTrigger.eta` is the dynamic event-trigger memory.
+
+## Quick Start
+
+```python
+from safe_control_gym.controllers.networked import (
+    DynamicPETCTrigger,
+    NetworkedControllerWrapper,
+    uniform_delay,
+)
+
+ctrl = make(config.algo, env_func, **config.algo_config)
+networked_ctrl = NetworkedControllerWrapper(
+    ctrl,
+    observation_dim=env.observation_space.shape[0],
+    sample_period_steps=1,
+    max_delay_steps=3,
+    delay_sampler=uniform_delay(3),
+    trigger=DynamicPETCTrigger(error_weight=1.0, signal_weight=0.03),
+)
+```
+
+Use `networked_ctrl` anywhere a safe-control-gym controller instance is accepted.
+
+## Before/After Evaluation
+
+Run three conditions with the same task, seed, and base controller:
+
+1. Direct controller with fresh observations.
+2. Periodic controller with fixed delayed transmissions.
+3. Dynamic PETC wrapper with the same delay sampler.
+
+Report:
+
+- task return or tracking error
+- transmission count
+- event rate
+- mean stale steps
+- max queue depth
+- safety or constraint violations when the task exposes them
+
+## Example
+
+```bash
+python examples/networked_control/run_dynamic_petc.py --max-delay-steps 3
+```
+
+The example uses a small local double-integrator task. It is a smoke test for
+the wrapper, not a reproduction of a paper simulation.
+
+To run a configured safe-control-gym controller behind the wrapper:
+
+```bash
+python examples/networked_control/networked_lqr_experiment.py \
+  --algo lqr \
+  --task cartpole \
+  --overrides ./examples/lqr/config_overrides/cartpole/cartpole_stab.yaml \
+              ./examples/lqr/config_overrides/cartpole/lqr_cartpole_stab.yaml
+```
+
+The integration point is intentionally small: create the normal controller, then
+wrap it with `NetworkedControllerWrapper` before constructing `BaseExperiment`.
+
+## Mobile Robot MPC Demo
+
+The mobile robot MPC example demonstrates a concrete robotics use case: a
+unicycle/differential-drive robot tracks a smooth path while localization
+observations arrive through the dynamic PETC wrapper.
+
+```bash
+python examples/networked_control/run_mobile_robot_mpc_petc.py \
+  --steps 260 \
+  --max-delay-steps 4
+```
+
+The controller is a dependency-light finite-horizon MPC implemented by sampling
+bounded control sequences and rolling out the kinematic model. It uses:
+
+```text
+state: [x, y, theta]
+control: [linear velocity, angular velocity]
+speed bounds: [0.0, 1.0] m/s
+angular velocity bounds: [-2.2, 2.2] rad/s
+horizon: 12 steps
+candidate sequences: 384
+```
+
+One representative run produced:
+
+```text
+Fresh MPC RMS tracking error: 0.0513 m
+PETC MPC RMS tracking error: 0.0565 m
+Transmissions: 75 / 260 samples
+Event rate: 28.85%
+Mean stale steps: 4.22
+Max queue depth: 4
+```
+
+This illustrates the intended engineering tradeoff: the wrapped MPC used about
+29% of the observation transmissions while keeping the RMS path-tracking error
+within 5.3 mm of the fresh-observation baseline in this scenario.
+
+## Limits
+
+The wrapper exposes certificate-like quantities such as update error, queue
+depth, and trigger margin. It does not claim plant-level input-to-state
+stability unless the user supplies compatible Lyapunov/error weights for the
+task.

examples/networked_control/networked_lqr_experiment.py

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+'''Run an existing safe-control-gym controller through a networked wrapper.'''
+
+from functools import partial
+import sys
+from pathlib import Path
+
+import numpy as np
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from safe_control_gym.controllers.networked import DynamicPETCTrigger, NetworkedControllerWrapper, uniform_delay
+from safe_control_gym.experiments.base_experiment import BaseExperiment
+from safe_control_gym.utils.configuration import ConfigFactory
+from safe_control_gym.utils.registration import make
+
+
+def run(gui=False, n_episodes=1, n_steps=None, max_delay_steps=3, sample_period_steps=1, seed=0):
+    '''Run the configured controller with event-triggered delayed observations.
+
+    The configured controller can be LQR, iLQR, MPC, or another controller with
+    the standard safe-control-gym select_action(obs, info) method.
+    '''
+
+    config = ConfigFactory().merge()
+    env_func = partial(make, config.task, **config.task_config)
+    random_env = env_func(gui=False)
+
+    base_ctrl = make(config.algo, env_func, **config.algo_config)
+    rng = np.random.default_rng(seed)
+
+    all_network_metrics = []
+    n_episodes = 1 if n_episodes is None else n_episodes
+
+    for _ in range(n_episodes):
+        init_obs, _ = random_env.reset()
+        static_env = env_func(gui=gui, randomized_init=False, init_state=init_obs)
+        static_train_env = env_func(gui=False, randomized_init=False, init_state=init_obs)
+
+        obs_dim = int(np.asarray(init_obs).reshape(-1).size)
+        ctrl = NetworkedControllerWrapper(
+            base_ctrl,
+            observation_dim=obs_dim,
+            sample_period_steps=sample_period_steps,
+            max_delay_steps=max_delay_steps,
+            delay_sampler=uniform_delay(max_delay_steps, rng=rng),
+            trigger=DynamicPETCTrigger(
+                error_weight=1.0,
+                signal_weight=0.03,
+                eta_decay=0.02,
+                eta_growth=0.02,
+                eta_skip_gain=0.15,
+                eta_transmit_drop=0.3,
+                eta_max=0.5,
+            ),
+        )
+        ctrl.reset_before_run(obs=init_obs, env=static_env)
+
+        experiment = BaseExperiment(env=static_env, ctrl=ctrl, train_env=static_train_env)
+        experiment.launch_training()
+        if n_steps is None:
+            trajs_data, _ = experiment.run_evaluation(training=True, n_episodes=1)
+        else:
+            trajs_data, _ = experiment.run_evaluation(training=True, n_steps=n_steps)
+
+        metrics = experiment.compute_metrics(trajs_data)
+        network_metrics = ctrl.metrics.summary()
+        all_network_metrics.append(network_metrics)
+
+        print('FINAL METRICS - ' + ', '.join([f'{key}: {value}' for key, value in metrics.items()]))
+        print('NETWORK METRICS - ' + ', '.join([f'{key}: {value}' for key, value in network_metrics.items()]))
+
+        static_env.close()
+        static_train_env.close()
+
+    base_ctrl.close()
+    random_env.close()
+    return all_network_metrics
+
+
+if __name__ == '__main__':
+    run()

examples/networked_control/run_dynamic_petc.py

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+'''Minimal dynamic PETC wrapper demonstration.
+
+This example intentionally uses a small local plant instead of recreating a
+paper example. In safe-control-gym, replace ProportionalController with an LQR,
+MPC, PPO, SAC, or PID controller instance and keep the wrapper unchanged.
+'''
+
+import argparse
+import sys
+from pathlib import Path
+
+import numpy as np
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+from safe_control_gym.controllers.networked import DynamicPETCTrigger, NetworkedControllerWrapper, uniform_delay
+
+
+class ProportionalController:
+    '''Small controller with the same select_action shape used by safe-control-gym.'''
+
+    def select_action(self, obs, info=None):
+        obs = np.asarray(obs, dtype=float)
+        return np.array([-0.8 * obs[0] - 0.25 * obs[1]])
+
+    def reset(self):
+        return None
+
+    def close(self):
+        return None
+
+
+def simulate(controller, n_steps=200, dt=0.05):
+    '''Run a double-integrator stabilization task.'''
+    x = np.array([2.0, 0.0])
+    trajectory = []
+    for step in range(n_steps):
+        action = np.asarray(controller.select_action(x, info={'current_step': step}), dtype=float)
+        u = float(np.clip(action[0], -3.0, 3.0))
+        x = np.array([x[0] + dt * x[1], x[1] + dt * u])
+        trajectory.append((step, x[0], x[1], u))
+    return np.asarray(trajectory)
+
+
+def rms_position(trajectory):
+    return float(np.sqrt(np.mean(trajectory[:, 1] ** 2)))
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--steps', type=int, default=200)
+    parser.add_argument('--max-delay-steps', type=int, default=3)
+    parser.add_argument('--sample-period-steps', type=int, default=1)
+    parser.add_argument('--seed', type=int, default=4)
+    args = parser.parse_args()
+
+    rng = np.random.default_rng(args.seed)
+    base = ProportionalController()
+    direct = simulate(base, n_steps=args.steps)
+
+    networked = NetworkedControllerWrapper(
+        ProportionalController(),
+        observation_dim=2,
+        sample_period_steps=args.sample_period_steps,
+        max_delay_steps=args.max_delay_steps,
+        delay_sampler=uniform_delay(args.max_delay_steps, rng=rng),
+        trigger=DynamicPETCTrigger(
+            error_weight=1.0,
+            signal_weight=0.03,
+            eta_decay=0.02,
+            eta_growth=0.02,
+            eta_skip_gain=0.15,
+            eta_transmit_drop=0.3,
+            eta_max=0.5,
+        ),
+    )
+    networked.reset_before_run(obs=np.array([2.0, 0.0]))
+    delayed = simulate(networked, n_steps=args.steps)
+
+    print('direct_rms_position:', f'{rms_position(direct):.4f}')
+    print('networked_rms_position:', f'{rms_position(delayed):.4f}')
+    print('networked_metrics:', networked.metrics.summary())
+
+
+if __name__ == '__main__':
+    main()