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SYNAPSE-X Environment
OpenEnv-style environment for predictive task scheduling under risk,
uncertainty, deadlines, and resource constraints.
"""
from copy import deepcopy
from typing import Any, Optional
from env.echo import ECHO
from env.models import Action, Observation, StepResult, Task
from env.prism import PRISM
from env.reward import RewardEngine
class SynapseXEnvironment:
"""
SYNAPSE-X predictive decision intelligence environment.
"""
MAX_STEPS = 30
MAX_RESOURCES = 1.0
MAX_DEADLINE = 20.0
REWARD_MIN = -2.0
REWARD_MAX = 2.0
COMPLETION_BONUS = 2.0
CASCADE_DELAY_ALPHA = 0.03
CASCADE_DEPENDENCY_BETA = 0.12
CASCADE_PRESSURE_GAMMA = 0.05
CASCADE_HIDDEN_DELAY = 0.06
CASCADE_MEMORY_DECAY = 0.85
CASCADE_MEMORY_FEEDBACK = 0.05
CASCADE_PHASE_THRESHOLD = 0.75
CASCADE_PHASE_MULTIPLIER = 1.2
CASCADE_PRESSURE_DEADLINE_ACCEL = 0.25
CASCADE_RESOURCE_CONTENTION = 0.12
def __init__(self, task_config: Optional[list[dict[str, Any]]] = None, seed: int = 42):
self.seed = seed
self.task_config = task_config or self._default_task_config()
self.echo = ECHO(max_deadline=self.MAX_DEADLINE)
self.prism = PRISM(seed=seed)
self.reward_engine = RewardEngine()
self._tasks: list[Task] = []
self._time = 0
self._resources = self.MAX_RESOURCES
self._total_reward = 0.0
self._history: list[dict[str, Any]] = []
self._done = False
self._dependency_graph = {
cfg["id"]: list(cfg.get("dependencies", []))
for cfg in self.task_config
}
self._hidden_penalties = {cfg["id"]: 0.0 for cfg in self.task_config}
self._cascade_mode = any(self._dependency_graph.values())
def reset(self) -> Observation:
self.prism = PRISM(seed=self.seed)
self._time = 0
self._resources = self.MAX_RESOURCES
self._total_reward = 0.0
self._history = []
self._done = False
self._hidden_penalties = {cfg["id"]: 0.0 for cfg in self.task_config}
self._tasks = []
for cfg in sorted(self.task_config, key=lambda item: item["id"]):
task = Task(**cfg)
task.released = task.release_time <= self._time
self.echo.predict(task, self._time)
self._tasks.append(task)
return self._build_observation()
def step(self, action: Action) -> StepResult:
if self._done:
raise RuntimeError("Episode is done. Call reset() first.")
action_type = getattr(action, "action_type", None)
task_id = getattr(action, "task_id", None)
if action_type not in {"execute", "delay", "reallocate"}:
return StepResult(
observation=self._build_observation(),
reward=-0.2,
done=self._done,
info={
"error": "invalid_action",
"task_id": task_id,
"action": action_type,
},
)
task = self._get_task(task_id)
if task is None:
return StepResult(
observation=self._build_observation(),
reward=-0.2,
done=self._done,
info={
"error": "invalid_task",
"task_id": task_id,
"action": action_type,
},
)
if not task.released:
return StepResult(
observation=self._build_observation(),
reward=-0.15,
done=self._done,
info={
"error": "task_unavailable",
"task_id": task_id,
"action": action_type,
"release_time": task.release_time,
},
)
reward = 0.0
info: dict[str, Any] = {"action": action_type, "task_id": task_id}
if action_type == "execute":
if task.is_terminal:
reward = -0.1
info["result"] = "already_terminal"
elif self._resources < task.resources_required:
reward = -0.3
info["result"] = "insufficient_resources"
else:
if self._cascade_mode:
self._apply_execution_pressure(task)
success = self.prism.execution_succeeds(task)
self._resources = max(0.0, self._resources - task.resources_required)
if success:
task.completed = True
info["result"] = "success"
else:
task.failed = True
info["result"] = "failure"
reward = self.reward_engine.compute(
task=task,
success=success,
current_time=self._time,
resources=self._resources,
action_type="execute",
)
elif action_type == "delay":
if task.is_terminal:
reward = -0.1
info["result"] = "already_terminal"
else:
task.deadline = max(0.0, task.deadline - 1.0)
task.delay_count += 1
reward = self.reward_engine.compute(
task=task,
success=False,
current_time=self._time,
resources=self._resources,
action_type="delay",
)
info["result"] = "delayed"
elif action_type == "reallocate":
boost = min(0.2, self.MAX_RESOURCES - self._resources)
self._resources = min(self.MAX_RESOURCES, self._resources + boost)
if boost <= 0.0:
reward = -0.05
info["result"] = "resources_full"
else:
reward = self.reward_engine.compute(
task=task,
success=False,
current_time=self._time,
resources=self._resources,
action_type="reallocate",
)
info["result"] = "reallocated"
info["resource_boost"] = round(boost, 4)
self._time += 1
self._release_available_tasks()
system_pressure = self._apply_cascade_dynamics(trigger_task=task, action_type=action_type)
for current_task in self._tasks:
if current_task.is_active:
deadline_decay = 1.0
if self._cascade_mode:
deadline_decay += self.CASCADE_PRESSURE_DEADLINE_ACCEL * system_pressure
current_task.deadline = max(0.0, current_task.deadline - deadline_decay)
if current_task.deadline <= 0.0:
current_task.failed = True
for current_task in self._tasks:
self.echo.predict(current_task, self._time)
all_terminal = all(current_task.is_terminal for current_task in self._tasks)
all_completed = all(current_task.completed for current_task in self._tasks)
time_up = self._time >= self.MAX_STEPS
self._done = all_terminal or time_up
if self._cascade_mode:
reward += self._cascade_reward_adjustment(task, action_type, system_pressure)
if all_completed:
reward += self.COMPLETION_BONUS
info["terminal_bonus"] = self.COMPLETION_BONUS
reward = self._clamp_reward(reward)
self._total_reward += reward
if self._done:
info["done_reason"] = "all_tasks_terminal" if all_terminal else "time_limit"
info["all_tasks_completed"] = all_completed
observation = self._build_observation(done=self._done)
self._history.append({"step": self._time, "reward": reward, "info": info})
return StepResult(observation=observation, reward=reward, done=self._done, info=info)
def state(self) -> dict[str, Any]:
return {
"time": self._time,
"resources": round(self._resources, 4),
"total_reward": round(self._total_reward, 4),
"done": self._done,
"tasks": [task.model_dump() for task in self._sorted_tasks()],
"history": self._history,
"seed": self.seed,
}
def _build_observation(self, done: bool = False) -> Observation:
return Observation(
tasks=deepcopy(self._sorted_tasks()),
time=self._time,
resources=round(self._resources, 4),
episode_done=done,
)
def _sorted_tasks(self) -> list[Task]:
return sorted(self._tasks, key=lambda task: task.id)
def _clamp_reward(self, reward: float) -> float:
return round(max(self.REWARD_MIN, min(self.REWARD_MAX, reward)), 4)
def _get_task(self, task_id: int) -> Optional[Task]:
for task in self._tasks:
if task.id == task_id:
return task
return None
def _release_available_tasks(self) -> None:
for task in self._tasks:
if not task.released and task.release_time <= self._time:
task.released = True
def _apply_execution_pressure(self, task: Task) -> None:
pressure = self._compute_system_pressure()
dependency_penalty = self._dependency_penalty(task)
hidden_penalty = self._hidden_penalties.get(task.id, 0.0)
contention_penalty = max(0.0, task.resources_required - self._resources) * self.CASCADE_RESOURCE_CONTENTION
pressure_penalty = self.CASCADE_PRESSURE_GAMMA * (pressure**2)
task.risk = min(
1.0,
task.risk + dependency_penalty + hidden_penalty + pressure_penalty + contention_penalty,
)
def _apply_cascade_dynamics(self, trigger_task: Optional[Task], action_type: str) -> float:
if not self._cascade_mode:
return 0.0
if action_type == "delay" and trigger_task is not None and trigger_task.is_active:
self._hidden_penalties[trigger_task.id] += self.CASCADE_HIDDEN_DELAY
active_tasks = [task for task in self._tasks if task.is_active]
if not active_tasks:
return 0.0
system_pressure = self._compute_system_pressure(active_tasks)
pressure_field = system_pressure**2
for task in active_tasks:
delay_penalty = self.CASCADE_DELAY_ALPHA * task.delay_count
dependency_penalty = self._dependency_penalty(task)
hidden_penalty = self._hidden_penalties.get(task.id, 0.0) * system_pressure
task.risk = min(
1.0,
task.risk + delay_penalty + dependency_penalty + self.CASCADE_PRESSURE_GAMMA * pressure_field + hidden_penalty,
)
task._history_risk = self.CASCADE_MEMORY_DECAY * task._history_risk + task.risk
task.risk = min(1.0, task.risk + self.CASCADE_MEMORY_FEEDBACK * task._history_risk)
system_pressure = self._compute_system_pressure(active_tasks)
if system_pressure > self.CASCADE_PHASE_THRESHOLD:
for task in active_tasks:
task.uncertainty = min(1.0, task.uncertainty * self.CASCADE_PHASE_MULTIPLIER)
return system_pressure
def _compute_system_pressure(self, tasks: Optional[list[Task]] = None) -> float:
active_tasks = tasks or [task for task in self._tasks if task.is_active]
if not active_tasks:
return 0.0
weighted_pressure = sum(task.risk * (1.0 + task.deadline_pressure) for task in active_tasks)
raw_pressure = weighted_pressure / len(active_tasks)
return raw_pressure / (1.0 + raw_pressure)
def _dependency_penalty(self, task: Task) -> float:
if not self._cascade_mode:
return 0.0
parents = self._dependency_graph.get(task.id, [])
if not parents:
return 0.0
penalty = 0.0
for parent_id in parents:
parent = self._get_task(parent_id)
if parent is None:
continue
penalty += 0.05 * parent.delay_count
if parent.failed:
penalty += self.CASCADE_DEPENDENCY_BETA
return penalty
def _cascade_reward_adjustment(self, task: Task, action_type: str, system_pressure: float) -> float:
failed_dependencies = 0
for parent_id in self._dependency_graph.get(task.id, []):
parent = self._get_task(parent_id)
if parent is not None and parent.failed:
failed_dependencies += 1
cascade_penalty = 0.5 * failed_dependencies
pressure_penalty = 0.1 * system_pressure
hidden_delay_penalty = 0.05 * self._hidden_penalties.get(task.id, 0.0)
adjustment = -(cascade_penalty + pressure_penalty + hidden_delay_penalty)
if action_type == "delay":
adjustment -= 0.1 * task.delay_count
return adjustment
def _default_task_config(self) -> list[dict[str, Any]]:
return [
{
"id": 0,
"name": "Low-Risk Report Generation",
"priority": 0.5,
"risk": 0.1,
"uncertainty": 0.1,
"deadline": 15.0,
"resources_required": 0.1,
}
]
@property
def total_reward(self) -> float:
return self._total_reward
@property
def time(self) -> int:
return self._time
@property
def done(self) -> bool:
return self._done
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