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3dfe1aa673
NuCon/nucon/rl.py
Dominik Roth 3dfe1aa673 fix: flat Box action space, SB3/HER compatibility, sim uninitialized param defaults 
rl.py:
- Action space is now a flat Box (SAC/PPO require this, not Dict)
- _build_flat_action_space + _unflatten_action helpers shared by both envs
- Params with undefined bounds excluded from action space (SAC needs finite bounds)
- Fix _build_param_space: use `is not None` check instead of falsy `or` (0 is valid min_val)
- NuconGoalEnv obs params default to simulator.model.input_params when sim provided;
  obs_params kwarg overrides for real-game deployment with same param set
- SIM_UNCERTAINTY kept out of policy obs vector (not available at deployment);
  available in reward_obs passed to objectives/terminators/reward_fn
- _read_obs returns (gym_obs, reward_obs) cleanly instead of smuggling via dict
- NuconGoalEnv additional_objectives wired into step()

sim.py:
- Uninitialized params return type-default (0/False/first-enum) instead of "None"
- Enum params serialised as integer value, not repr string

README.md:
- Fix HerReplayBuffer import path (sb3 2.x: her.her_replay_buffer)
- Remove non-existent simulator.run() call
- Fix broken anchor links, remove "work in progress" from intro

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-03-12 19:16:07 +01:00

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import inspect
import gymnasium as gym
from gymnasium import spaces
import numpy as np
import time
from typing import Dict, Any, Callable, List, Optional
from enum import Enum
from nucon import Nucon, BreakerStatus, PumpStatus, PumpDryStatus, PumpOverloadStatus
# ---------------------------------------------------------------------------
# Reward / objective helpers
# ---------------------------------------------------------------------------
Objectives = {
"null": lambda obs: 0,
"max_power": lambda obs: obs["GENERATOR_0_KW"] + obs["GENERATOR_1_KW"] + obs["GENERATOR_2_KW"],
"episode_time": lambda obs: obs["EPISODE_TIME"],
}
def _uncertainty_penalty(start=0.3, scale=1.0, mode='l2'):
excess = lambda obs: max(0.0, obs.get('SIM_UNCERTAINTY', 0.0) - start)
if mode == 'l2':
return lambda obs: -scale * excess(obs) ** 2
elif mode == 'linear':
return lambda obs: -scale * excess(obs)
else:
raise ValueError(f"Unknown mode '{mode}'. Use 'l2' or 'linear'.")
def _uncertainty_abort(threshold=0.7):
return lambda obs: 1.0 if obs.get('SIM_UNCERTAINTY', 0.0) >= threshold else 0.0
Parameterized_Objectives = {
"target_temperature": lambda goal_temp: lambda obs: -((obs["CORE_TEMP"] - goal_temp) ** 2),
"target_gap": lambda goal_gap: lambda obs: -((obs["CORE_TEMP"] - obs["CORE_TEMP_MIN"] - goal_gap) ** 2),
"temp_below": lambda max_temp: lambda obs: -(np.clip(obs["CORE_TEMP"] - max_temp, 0, np.inf) ** 2),
"temp_above": lambda min_temp: lambda obs: -(np.clip(min_temp - obs["CORE_TEMP"], 0, np.inf) ** 2),
"constant": lambda constant: lambda obs: constant,
"uncertainty_penalty": _uncertainty_penalty, # (start, scale, mode) -> (obs) -> float
}
Parameterized_Terminators = {
"uncertainty_abort": _uncertainty_abort, # (threshold,) -> (obs) -> float
}
# ---------------------------------------------------------------------------
# Internal helpers
# ---------------------------------------------------------------------------
def _build_flat_action_space(nucon, obs_param_set=None):
"""Return (Box, ordered_param_ids) for all writable, readable, non-cheat params.
If obs_param_set is provided, only include params in that set.
"""
params = []
lows, highs = [], []
for param_id, param in nucon.get_all_writable().items():
if not param.is_readable or param.is_cheat:
continue
if obs_param_set is not None and param_id not in obs_param_set:
continue
if param.min_val is None or param.max_val is None:
continue # SAC requires finite action bounds
sp = _build_param_space(param)
if sp is None:
continue
params.append(param_id)
lows.append(sp.low[0])
highs.append(sp.high[0])
box = spaces.Box(low=np.array(lows, dtype=np.float32),
high=np.array(highs, dtype=np.float32), dtype=np.float32)
return box, params
def _unflatten_action(flat_action, param_ids):
return {pid: float(flat_action[i]) for i, pid in enumerate(param_ids)}
def _build_param_space(param):
"""Return a gymnasium Box for a single NuconParameter, or None if unsupported."""
if param.param_type in (float, int):
lo = param.min_val if param.min_val is not None else -np.inf
hi = param.max_val if param.max_val is not None else np.inf
return spaces.Box(low=lo, high=hi, shape=(1,), dtype=np.float32)
elif param.param_type == bool:
return spaces.Box(low=0, high=1, shape=(1,), dtype=np.float32)
elif param.param_type == str:
return None
elif issubclass(param.param_type, Enum):
return spaces.Box(low=0, high=len(param.param_type) - 1, shape=(1,), dtype=np.float32)
return None
def _apply_action(nucon, action):
for param_id, value in action.items():
param = nucon._parameters[param_id]
if issubclass(param.param_type, Enum):
value = param.param_type(int(np.asarray(value).flat[0]))
else:
value = param.param_type(np.asarray(value).flat[0])
if param.min_val is not None and param.max_val is not None:
value = np.clip(value, param.min_val, param.max_val)
nucon.set(param, value)
# ---------------------------------------------------------------------------
# NuconEnv
# ---------------------------------------------------------------------------
class NuconEnv(gym.Env):
metadata = {'render_modes': ['human']}
def __init__(self, nucon=None, simulator=None, render_mode=None, seconds_per_step=5,
objectives=['null'], terminators=['null'], objective_weights=None, terminate_above=0):
super().__init__()
self.render_mode = render_mode
self.seconds_per_step = seconds_per_step
if objective_weights is None:
objective_weights = [1.0 for _ in objectives]
self.objective_weights = objective_weights
self.terminate_above = terminate_above
self.simulator = simulator
if nucon is None:
nucon = Nucon(port=simulator.port) if simulator else Nucon()
self.nucon = nucon
# Observation space — SIM_UNCERTAINTY included when a simulator is present
obs_spaces = {'EPISODE_TIME': spaces.Box(low=0, high=np.inf, shape=(1,), dtype=np.float32)}
if simulator is not None:
obs_spaces['SIM_UNCERTAINTY'] = spaces.Box(low=0.0, high=1.0, shape=(1,), dtype=np.float32)
for param_id, param in self.nucon.get_all_readable().items():
sp = _build_param_space(param)
if sp is not None:
obs_spaces[param_id] = sp
self.observation_space = spaces.Dict(obs_spaces)
self.action_space, self._action_params = _build_flat_action_space(self.nucon)
self.objectives = []
self.terminators = []
for objective in objectives:
if objective in Objectives:
self.objectives.append(Objectives[objective])
elif callable(objective):
self.objectives.append(objective)
else:
raise ValueError(f"Unsupported objective: {objective}")
for terminator in terminators:
if terminator in Objectives:
self.terminators.append(Objectives[terminator])
elif callable(terminator):
self.terminators.append(terminator)
else:
raise ValueError(f"Unsupported terminator: {terminator}")
def _get_obs(self, sim_uncertainty=None):
obs = {}
for param_id, param in self.nucon.get_all_readable().items():
if param.param_type == str or param_id not in self.observation_space.spaces:
continue
value = self.nucon.get(param_id)
if isinstance(value, Enum):
value = value.value
obs[param_id] = value
obs['EPISODE_TIME'] = self._total_steps * self.seconds_per_step
if 'SIM_UNCERTAINTY' in self.observation_space.spaces:
obs['SIM_UNCERTAINTY'] = sim_uncertainty if sim_uncertainty is not None else 0.0
return obs
def _get_info(self, obs):
info = {'objectives': {}, 'objectives_weighted': {}}
for objective, weight in zip(self.objectives, self.objective_weights):
obj = objective(obs)
name = getattr(objective, '__name__', repr(objective))
info['objectives'][name] = obj
info['objectives_weighted'][name] = obj * weight
return info
def reset(self, seed=None, options=None):
super().reset(seed=seed)
self._total_steps = 0
observation = self._get_obs()
return observation, self._get_info(observation)
def step(self, action):
_apply_action(self.nucon, _unflatten_action(action, self._action_params))
# Advance sim (or sleep) — get uncertainty for obs injection
truncated = False
uncertainty = None
if self.simulator:
uncertainty = self.simulator.update(self.seconds_per_step, return_uncertainty=True)
else:
sim_speed = self.nucon.GAME_SIM_SPEED.value or 1.0
time.sleep(self.seconds_per_step / sim_speed)
self._total_steps += 1
observation = self._get_obs(sim_uncertainty=uncertainty)
info = self._get_info(observation)
reward = sum(obj for obj in info['objectives_weighted'].values())
terminated = np.sum([t(observation) for t in self.terminators]) > self.terminate_above
return observation, reward, terminated, truncated, info
def render(self):
pass
def close(self):
pass
def _flatten_observation(self, observation):
return np.concatenate([np.asarray(v).flatten() for v in observation.values()])
# ---------------------------------------------------------------------------
# NuconGoalEnv
# ---------------------------------------------------------------------------
class NuconGoalEnv(gym.Env):
"""
Goal-conditioned reactor environment compatible with SB3 HER (Hindsight Experience Replay).
Observation is a Dict with three keys:
- 'observation': all readable non-goal, non-str params + SIM_UNCERTAINTY (when sim active)
- 'achieved_goal': current values of goal_params, normalised to [0, 1] within goal_range
- 'desired_goal': target values sampled each episode, normalised to [0, 1]
``SIM_UNCERTAINTY`` in 'observation' lets reward_fn / terminators reference uncertainty directly.
reward_fn signature: ``(achieved, desired)`` or ``(achieved, desired, obs)`` — the 3-arg form
receives the full observation dict (including SIM_UNCERTAINTY) for uncertainty-aware shaping.
Usage with SB3 HER::
from stable_baselines3 import SAC
from stable_baselines3.common.buffers import HerReplayBuffer
from nucon.rl import NuconGoalEnv, UncertaintyPenalty, UncertaintyAbort
env = NuconGoalEnv(
goal_params=['GENERATOR_0_KW', 'GENERATOR_1_KW', 'GENERATOR_2_KW'],
goal_range={'GENERATOR_0_KW': (0, 1200), 'GENERATOR_1_KW': (0, 1200), 'GENERATOR_2_KW': (0, 1200)},
tolerance=0.05,
simulator=simulator,
# uncertainty-aware reward: penalise OOD, abort if too far out
reward_fn=lambda ag, dg, obs: (
-(np.linalg.norm(ag - dg) ** 2)
- 2.0 * max(0, obs.get('SIM_UNCERTAINTY', 0) - 0.3) ** 2
),
terminators=[UncertaintyAbort(threshold=0.7)],
)
model = SAC('MultiInputPolicy', env, replay_buffer_class=HerReplayBuffer)
model.learn(total_timesteps=500_000)
"""
metadata = {'render_modes': ['human']}
def __init__(
self,
goal_params,
goal_range=None,
reward_fn=None,
tolerance=None,
nucon=None,
simulator=None,
render_mode=None,
seconds_per_step=5,
terminators=None,
terminate_above=0,
additional_objectives=None,
additional_objective_weights=None,
obs_params=None,
):
super().__init__()
self.render_mode = render_mode
self.seconds_per_step = seconds_per_step
self.terminate_above = terminate_above
self.simulator = simulator
self.goal_params = list(goal_params)
self.tolerance = tolerance
if nucon is None:
nucon = Nucon(port=simulator.port) if simulator else Nucon()
self.nucon = nucon
all_readable = self.nucon.get_all_readable()
for pid in self.goal_params:
if pid not in all_readable:
raise ValueError(f"Goal param '{pid}' is not a readable parameter")
goal_range = goal_range or {}
self._goal_low = np.array([
goal_range.get(pid, (all_readable[pid].min_val or 0.0, all_readable[pid].max_val or 1.0))[0]
for pid in self.goal_params
], dtype=np.float32)
self._goal_high = np.array([
goal_range.get(pid, (all_readable[pid].min_val or 0.0, all_readable[pid].max_val or 1.0))[1]
for pid in self.goal_params
], dtype=np.float32)
self._goal_range = self._goal_high - self._goal_low
self._goal_range[self._goal_range == 0] = 1.0
# Detect reward_fn arity for backward compat (2-arg vs 3-arg)
self._reward_fn = reward_fn
if reward_fn is not None:
n_args = len(inspect.signature(reward_fn).parameters)
self._reward_fn_wants_obs = n_args >= 3
else:
self._reward_fn_wants_obs = False
# Observation params: model.input_params defines the canonical list — the same set is
# used whether training in sim or deploying to the real game (the game simply has more
# params available; we query only the subset we care about).
# Explicit obs_params overrides everything (use when deploying to real game without sim).
# SB3 HER requires observation to be a flat Box, not a nested Dict.
goal_set = set(self.goal_params)
self._obs_with_uncertainty = simulator is not None
if obs_params is not None:
base_params = [p for p in obs_params if p not in goal_set]
elif simulator is not None and hasattr(simulator, 'model') and simulator.model is not None:
base_params = [p for p in simulator.model.input_params
if p not in goal_set and p in all_readable
and _build_param_space(all_readable[p]) is not None]
else:
base_params = [p for p, param in all_readable.items()
if p not in goal_set and _build_param_space(param) is not None]
# SIM_UNCERTAINTY is not in _obs_params — it's not available at deployment on the real game
self._obs_params = base_params
n_goals = len(self.goal_params)
self.observation_space = spaces.Dict({
'observation': spaces.Box(low=-np.inf, high=np.inf,
shape=(len(self._obs_params),), dtype=np.float32),
'achieved_goal': spaces.Box(low=0.0, high=1.0, shape=(n_goals,), dtype=np.float32),
'desired_goal': spaces.Box(low=0.0, high=1.0, shape=(n_goals,), dtype=np.float32),
})
# Action space: writable params within the obs param set (flat Box for SB3 compatibility).
self.action_space, self._action_params = _build_flat_action_space(self.nucon, set(base_params))
self._terminators = terminators or []
_objs = additional_objectives or []
self._objectives = [Objectives[o] if isinstance(o, str) else o for o in _objs]
self._objective_weights = additional_objective_weights or [1.0] * len(self._objectives)
self._desired_goal = np.zeros(n_goals, dtype=np.float32)
self._total_steps = 0
def compute_reward(self, achieved_goal, desired_goal, info):
"""Dense negative L2, sparse with tolerance, or custom reward_fn."""
obs_named = info.get('obs_named', {}) if isinstance(info, dict) else {}
if self._reward_fn is not None:
if self._reward_fn_wants_obs:
return self._reward_fn(achieved_goal, desired_goal, obs_named)
return self._reward_fn(achieved_goal, desired_goal)
dist = np.linalg.norm(achieved_goal - desired_goal, axis=-1)
if self.tolerance is not None:
return (dist <= self.tolerance).astype(np.float32) - 1.0
return -dist
def _read_goal_values(self):
raw = np.array([self.nucon.get(pid) or 0.0 for pid in self.goal_params], dtype=np.float32)
return np.clip((raw - self._goal_low) / self._goal_range, 0.0, 1.0)
def _read_obs(self, sim_uncertainty=None):
"""Return (gym_obs_dict, reward_obs_dict).
gym_obs_dict — flat Box observation for the policy (no SIM_UNCERTAINTY).
reward_obs_dict — same values plus SIM_UNCERTAINTY for objectives/terminators/reward_fn.
"""
reward_obs = {}
if self._obs_with_uncertainty:
reward_obs['SIM_UNCERTAINTY'] = float(sim_uncertainty) if sim_uncertainty is not None else 0.0
for param_id in self._obs_params:
value = self.nucon.get(param_id)
if isinstance(value, Enum):
value = value.value
reward_obs[param_id] = float(value) if value is not None else 0.0
obs_vec = np.array([reward_obs[p] for p in self._obs_params], dtype=np.float32)
achieved = self._read_goal_values()
gym_obs = {'observation': obs_vec, 'achieved_goal': achieved,
'desired_goal': self._desired_goal.copy()}
return gym_obs, reward_obs
def reset(self, seed=None, options=None):
super().reset(seed=seed)
self._total_steps = 0
rng = np.random.default_rng(seed)
self._desired_goal = rng.uniform(0.0, 1.0, size=len(self.goal_params)).astype(np.float32)
gym_obs, _ = self._read_obs()
return gym_obs, {}
def step(self, action):
_apply_action(self.nucon, _unflatten_action(action, self._action_params))
if self.simulator:
uncertainty = self.simulator.update(self.seconds_per_step, return_uncertainty=True)
else:
sim_speed = self.nucon.GAME_SIM_SPEED.value or 1.0
time.sleep(self.seconds_per_step / sim_speed)
uncertainty = None
self._total_steps += 1
gym_obs, reward_obs = self._read_obs(sim_uncertainty=uncertainty)
info = {'achieved_goal': gym_obs['achieved_goal'], 'desired_goal': gym_obs['desired_goal'],
'obs_named': reward_obs}
reward = float(self.compute_reward(gym_obs['achieved_goal'], gym_obs['desired_goal'], info))
reward += sum(w * o(reward_obs) for o, w in zip(self._objectives, self._objective_weights))
terminated = any(t(reward_obs) > self.terminate_above for t in self._terminators)
return gym_obs, reward, terminated, False, info
def render(self):
pass
def close(self):
pass
# ---------------------------------------------------------------------------
# Registration
# ---------------------------------------------------------------------------
def register_nucon_envs():
gym.register(
id='Nucon-max_power-v0',
entry_point='nucon.rl:NuconEnv',
kwargs={'seconds_per_step': 5, 'objectives': ['max_power']}
)
gym.register(
id='Nucon-target_temperature_350-v0',
entry_point='nucon.rl:NuconEnv',
kwargs={'seconds_per_step': 5, 'objectives': [Parameterized_Objectives['target_temperature'](goal_temp=350)]}
)
gym.register(
id='Nucon-safe_max_power-v0',
entry_point='nucon.rl:NuconEnv',
kwargs={'seconds_per_step': 5,
'objectives': [Parameterized_Objectives['temp_above'](min_temp=310),
Parameterized_Objectives['temp_below'](max_temp=365), 'max_power'],
'objective_weights': [1, 10, 1/100_000]}
)
gym.register(
id='Nucon-goal_power-v0',
entry_point='nucon.rl:NuconGoalEnv',
kwargs={
'goal_params': ['GENERATOR_0_KW', 'GENERATOR_1_KW', 'GENERATOR_2_KW'],
'goal_range': {'GENERATOR_0_KW': (0.0, 1200.0), 'GENERATOR_1_KW': (0.0, 1200.0), 'GENERATOR_2_KW': (0.0, 1200.0)},
'seconds_per_step': 5,
}
)
gym.register(
id='Nucon-goal_temp-v0',
entry_point='nucon.rl:NuconGoalEnv',
kwargs={
'goal_params': ['CORE_TEMP'],
'goal_range': {'CORE_TEMP': (280.0, 380.0)},
'seconds_per_step': 5,
}
)
register_nucon_envs()
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