Clarifies that the goal reward is the primary built-in objective;
additional_objectives are additive on top of it.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- NuconGoalEnv now accepts objectives/objective_weights; additive on top
of the goal reward, same interface as NuconEnv
- README: use UncertaintyPenalty/UncertaintyAbort correctly (via objectives
and terminators, not as constructor params that don't exist)
- Step 3 prose updated to reference composable callables
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add NuconGoalEnv for goal-conditioned HER training (SAC + HER)
- Add UncertaintyPenalty and UncertaintyAbort composable callables;
SIM_UNCERTAINTY injected into obs dict when simulator is active
- Fix rl.py: str-typed params crash, missing Enum import, write-only
params in action space, broken step() iteration order
- Remove uncertainty state from sim (return value from update() instead)
- Rename kNN -> kNN-GP throughout README; add model selection note
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
sim.py:
- simulator.update(return_uncertainty=True) calls forward_with_uncertainty
on kNN models and returns the GP std; returns None for NN or when not
requested (no extra cost if unused)
- No state stored on simulator; caller decides what to do with the value
rl.py (NuconEnv and NuconGoalEnv):
- uncertainty_penalty_start: above this GP std, subtract a linear penalty
from the reward (scaled by uncertainty_penalty_scale, default 1.0)
- uncertainty_abort: at or above this GP std, set truncated=True
- Only calls update(return_uncertainty=True) when either threshold is set
- Uncertainty only applies when using a simulator (kNN model); ignored otherwise
Example:
simulator = NuconSimulator()
simulator.load_model('reactor_knn.pkl')
env = NuconGoalEnv(..., simulator=simulator,
uncertainty_penalty_start=0.3,
uncertainty_abort=0.7,
uncertainty_penalty_scale=2.0)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
NuconSimulator now accepts uncertainty_threshold (default None = disabled).
When set and using a kNN model, _update_reactor_state() calls
forward_with_uncertainty() and raises HighUncertaintyError if the GP
posterior std exceeds the threshold.
NuconEnv and NuconGoalEnv catch HighUncertaintyError in step() and
return truncated=True, so SB3 bootstraps the value rather than treating
OOD regions as terminal states.
Usage:
simulator = NuconSimulator(uncertainty_threshold=0.3)
# episodes are cut short when the policy wanders OOD
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
rl.py:
- Add missing `from enum import Enum`
- Skip str-typed params in obs/action space construction (was crashing)
- Guard action space: exclude write-only (is_readable=False) and cheat params
- Fix step() param lookup (no longer iterates Nucon, uses _parameters dict directly)
- Correct sim-speed time dilation in real-game sleep
- Extract _build_param_space() helper shared by NuconEnv and NuconGoalEnv
- Add NuconGoalEnv: goal-conditioned env with normalised achieved/desired goal
vectors, compatible with SB3 HerReplayBuffer; goals sampled per episode
- Register Nucon-goal_power-v0 and Nucon-goal_temp-v0 presets
- Enum obs/action space now scalar index (not one-hot)
sim.py:
- Store self.port and self.host on NuconSimulator
- Add set_model() to accept a pre-loaded model directly
- load_model() detects type by extension (.pkl → kNN, else → NN torch)
and reads new checkpoint format with embedded input/output param lists
- _update_reactor_state() uses model.input_params (not all readable params),
calls .forward() directly for both NN and kNN, guards torch.no_grad per type
- Import ReactorKNNModel and pickle
model.py:
- save_model() embeds input_params/output_params in NN checkpoint dict
- load_model() handles new checkpoint format (state_dict key) with fallback
README.md:
- Update note: RODS_POS_ORDERED is no longer the only writable param;
game v2.2.25.213 exposes rod banks, pumps, MSCVs, switches and more
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
