Agent Conversation: deepseek-reasoner
Model: deepseek-reasoner
Total steps: 10
Messages: 23
UserStep 0
Task: safe-rl
Safe RL: Constraint-Handling Mechanism Design
Objective
Design a constraint-handling mechanism for safe reinforcement learning. Your code goes in custom_lag.py, a subclass of PPO registered as CustomLag. Reference implementations (PPOLag using Lagrange multiplier, CPPOPID using PID controller) are provided as read-only.
Background
Safe RL aims to maximize reward while satisfying safety constraints (keeping episode cost below a limit). The key challenge is how to adaptively balance reward and cost: the Lagrangian approach converts the constrained problem to an unconstrained dual problem via a multiplier lambda, while PID methods use control theory for more responsive constraint satisfaction. You must design: (1) a multiplier update rule in _update(), and (2) an advantage combination formula in _compute_adv_surrogate().
Evaluation
Evaluated on 3 Safety-Gymnasium environments to test generalization:
- SafetyPointGoal1-v0: point robot navigating to goals while avoiding hazards
- SafetyCarGoal1-v0: car robot (non-holonomic) navigating to goals while avoiding hazards
- SafetyPointButton1-v0: point robot pressing goal buttons while avoiding hazards
Metrics: episode reward (higher is better) and episode cost (lower is better, target <= 25.0). Each environment trains for 2M steps.
Baselines
- naive: no constraint handling (pure PPO, ignores cost)
- ppo_lag: Lagrangian multiplier updated via Adam optimizer
- pid_lag: PID controller for multiplier update
omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py [EDITABLE — lines 20–20, lines 48–70 only]
1: """Custom Lagrangian-based safe PPO for MLS-Bench.
2:
3: EDITABLE section: imports + constraint handling methods.
4: FIXED sections: algorithm registration, learn() with metrics reporting.
5: """
6:
7: from __future__ import annotations
8:
9: import time
10:
11: import numpy as np
12: import torch
13:
14: from omnisafe.algorithms import registry
15: from omnisafe.algorithms.on_policy.base.ppo import PPO
16:
17: # ===================================================================
18: # EDITABLE: Custom imports
19: # ===================================================================
20:
21:
22: # ===================================================================
23: # FIXED: Algorithm class definition
24: # ===================================================================
25: @registry.register
26: class CustomLag(PPO):
27: """Custom Lagrangian-based safe RL algorithm.
28:
29: Extends PPO with constraint handling for safe reinforcement learning.
30: The agent must design:
31: 1. _init: Initialize constraint handler state (call super()._init() first)
32: 2. _init_log: Register logging keys (call super()._init_log() first)
33: 3. _updaAssistantStep 1edit
Tool Input
1{
2 "op": "replace",
3 "filename": "omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py",
4 "start_line": 20,
5 "end_line": 20,
6 "content": "import math"
7}Tool ResultStep 1
OK: Replaced lines 20..20 with 1 line(s) in omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py. Editable range: 20–20, 48–70.
[Current file: omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py | editable: 20–20, 48–70 | total: 149 lines]
20 import math
...
48 def _init(self) -> None:
49 super()._init()
50 self._cost_limit: float = self._cfgs.lagrange_cfgs.cost_limit
... (17 more lines) ...
68 Agent should incorporate self._lagrangian_multiplier to penalize cost.
69 """
70 return adv_rAssistantStep 2edit
Tool Input
1{
2 "op": "replace",
3 "filename": "omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py",
4 "start_line": 48,
5 "end_line": 70,
6 "content": " def _init(self) -> None:\n super()._init()\n self._cost_limit: float = self._cfgs.lagrange_cfgs.cost_limit\n # Adaptive Lagrangian parameters\n self._lagrangian_multiplier: float = 0.0\n self._ema_delta: float = 0.0 # exponential moving average of constraint violation\n self._ema_beta: float = 0.9 # smoothing factor\n self._lambda_lr: float = self._cfgs.lagrange_cfgs.lambda_lr # base learning rate\n self._adaptive_lr: float = self._lambda_lr # current adaptive learning rate\n self._lr_scale: float = 1.2 # multiplicative factor for adapting learning rate\n self._lr_decay: float = 0.99 # decay factor for learning rate\n self._violation_threshold: float = 5.0 # threshold for adjusting learning rate\n self._temperature: float = 1.0 # temperature for tanh scaling of cost advantage\n\n def _init_log(self) -> None:\n super()._init_log()\n self._logger.register_key('Metrics/LagrangeMultiplier', min_and_max=True)\n self._logger.register_key('Metrics/EMADelta', min_and_max=True)\n self._logger.register_key('Metrics/AdaptiveLR', min_and_max=True)\n\n def _update(self) -> None:\n Jc = self._logger.get_stats('Metrics/EpCost')[0]\n assert not np.isnan(Jc), 'cost is nan'\n # Compute constraint violation\n delta = Jc - self._cost_limit\n # Update exponential moving average\n self._ema_delta = self._ema_beta * self._ema_delta + (1.0 - self._ema_beta) * delta\n # Adapt learning rate based on violation magnitude\n if abs(self._ema_delta) > self._violation_threshold:\n # Increase learning rate when violation is large\n self._adaptive_lr *= self._lr_scale\n else:\n # Decay learning rate when violation is small\n self._adaptive_lr *= self._lr_decay\n # Clip learning rate to reasonable range\n self._adaptive_lr = np.clip(self._adaptive_lr, 1e-5, 0.1)\n # Update Lagrangian multiplier\n self._lagrangian_multiplier = max(0.0, self._lagrangian_multiplier + self._adaptive_lr * self._ema_delta)\n # Log metrics\n self._logger.store({\n 'Metrics/LagrangeMultiplier': self._lagrangian_multiplier,\n 'Metrics/EMADelta': self._ema_delta,\n 'Metrics/AdaptiveLR': self._adaptive_lr,\n })\n super()._update()\n\n def _compute_adv_surrogate(self, adv_r: torch.Tensor, adv_c: torch.Tensor) -> torch.Tensor:\n \"\"\"Combine reward and cost advantages with smooth penalty.\"\"\"\n penalty = self._lagrangian_multiplier\n # Scale cost advantage with tanh to prevent extreme gradients\n scaled_adv_c = torch.tanh(adv_c / self._temperature) * adv_c\n # Combined advantage: reward - penalty * scaled cost\n combined = adv_r - penalty * scaled_adv_c\n # Normalize by (1 + penalty) for stability (similar to baselines)\n return combined / (1.0 + penalty)"
7}Tool ResultStep 2
OK: Replaced lines 48..70 with 55 line(s) in omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py. Editable range: 20–20, 48–102.
[Current file: omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py | editable: 20–20, 48–102 | total: 181 lines]
20 import math
...
48 def _init(self) -> None:
49 super()._init()
50 self._cost_limit: float = self._cfgs.lagrange_cfgs.cost_limit
... (49 more lines) ...
100 combined = adv_r - penalty * scaled_adv_c
101 # Normalize by (1 + penalty) for stability (similar to baselines)
102 return combined / (1.0 + penalty)AssistantStep 3test
Tool ResultStep 3
[Test #1] (2 tests remaining; call submit(n=N) to choose which test result to submit as final) ## Seed 42 ### SafetyPointGoal1-v0 (scripts/point_goal.sh) Training metrics (last epochs): TRAIN_METRICS epoch=95 ep_ret=-0.7960 ep_cost=98.4600 ep_len=1000.0 TRAIN_METRICS epoch=96 ep_ret=-0.8715 ep_cost=98.4000 ep_len=1000.0 TRAIN_METRICS epoch=97 ep_ret=-1.2125 ep_cost=81.1900 ep_len=1000.0 TRAIN_METRICS epoch=98 ep_ret=-1.1391 ep_cost=78.0000 ep_len=1000.0 TRAIN_METRICS epoch=99 ep_ret=-1.1802 ep_cost=73.5300 ep_len=1000.0 Final evaluation: TEST_METRICS ep_ret=-1.1802 ep_cost=73.5300 ep_len=1000.0 ### SafetyCarGoal1-v0 (scripts/car_goal.sh) Training metrics (last epochs): TRAIN_METRICS epoch=95 ep_ret=-0.1703 ep_cost=20.7900 ep_len=1000.0 TRAIN_METRICS epoch=96 ep_ret=-0.2268 ep_cost=21.5500 ep_len=1000.0 TRAIN_METRICS epoch=97 ep_ret=-0.2424 ep_cost=29.4600 ep_len=1000.0 TRAIN_METRICS epoch=98 ep_ret=-0.3878 ep_cost=37.8600 ep_len=1000.0 TRAIN_METRICS epoch=99 ep_ret=-0.4952 ep_cos
AssistantStep 4edit
Tool Input
1{
2 "op": "replace",
3 "filename": "omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py",
4 "start_line": 48,
5 "end_line": 102,
6 "content": " def _init(self) -> None:\n super()._init()\n self._cost_limit: float = self._cfgs.lagrange_cfgs.cost_limit\n # Adaptive Step Size Lagrangian parameters\n self._lagrangian_multiplier: float = 0.0\n self._ema_delta: float = 0.0 # exponential moving average of constraint violation\n self._ema_beta: float = 0.9 # smoothing factor\n self._base_lr: float = self._cfgs.lagrange_cfgs.lambda_lr # base learning rate\n self._alpha: float = 0.1 # scaling factor for adaptive step size\n self._max_lr: float = 0.1 # maximum learning rate\n self._min_lr: float = 1e-5 # minimum learning rate\n # For advantage combination\n self._clip_adv_c: float = 10.0 # clip cost advantage magnitude\n\n def _init_log(self) -> None:\n super()._init_log()\n self._logger.register_key('Metrics/LagrangeMultiplier', min_and_max=True)\n self._logger.register_key('Metrics/EMADelta', min_and_max=True)\n self._logger.register_key('Metrics/AdaptiveLR', min_and_max=True)\n\n def _update(self) -> None:\n Jc = self._logger.get_stats('Metrics/EpCost')[0]\n assert not np.isnan(Jc), 'cost is nan'\n # Compute constraint violation\n delta = Jc - self._cost_limit\n # Update exponential moving average\n self._ema_delta = self._ema_beta * self._ema_delta + (1.0 - self._ema_beta) * delta\n # Adaptive step size: larger violation → larger learning rate\n adaptive_lr = self._base_lr * (1.0 + self._alpha * abs(self._ema_delta))\n adaptive_lr = np.clip(adaptive_lr, self._min_lr, self._max_lr)\n # Update Lagrangian multiplier (gradient ascent on dual)\n self._lagrangian_multiplier = max(0.0, self._lagrangian_multiplier + adaptive_lr * delta)\n # Log metrics\n self._logger.store({\n 'Metrics/LagrangeMultiplier': self._lagrangian_multiplier,\n 'Metrics/EMADelta': self._ema_delta,\n 'Metrics/AdaptiveLR': adaptive_lr,\n })\n super()._update()\n\n def _compute_adv_surrogate(self, adv_r: torch.Tensor, adv_c: torch.Tensor) -> torch.Tensor:\n \"\"\"Combine reward and cost advantages with clipped cost advantage.\"\"\"\n penalty = self._lagrangian_multiplier\n # Clip cost advantage to avoid extreme gradients\n clipped_adv_c = torch.clamp(adv_c, -self._clip_adv_c, self._clip_adv_c)\n # Combined advantage: reward - penalty * cost\n combined = adv_r - penalty * clipped_adv_c\n # Normalize by (1 + penalty) for stability\n return combined / (1.0 + penalty)"
7}Tool ResultStep 4
OK: Replaced lines 48..102 with 49 line(s) in omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py. Editable range: 20–20, 48–96.
[Current file: omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py | editable: 20–20, 48–96 | total: 175 lines]
20 import math
...
48 def _init(self) -> None:
49 super()._init()
50 self._cost_limit: float = self._cfgs.lagrange_cfgs.cost_limit
... (43 more lines) ...
94 combined = adv_r - penalty * clipped_adv_c
95 # Normalize by (1 + penalty) for stability
96 return combined / (1.0 + penalty)AssistantStep 5test
AssistantStep 5test
Tool ResultStep 5
[Test #2] (0 tests remaining; call submit(n=N) to choose which test result to submit as final) [NOTE] This was your last test. You MUST now call submit(n=X) to choose which test result to submit as your final answer. ## Seed 42 ### SafetyPointGoal1-v0 (scripts/point_goal.sh) Training metrics (last epochs): TRAIN_METRICS epoch=95 ep_ret=-0.7960 ep_cost=98.4600 ep_len=1000.0 TRAIN_METRICS epoch=96 ep_ret=-0.8715 ep_cost=98.4000 ep_len=1000.0 TRAIN_METRICS epoch=97 ep_ret=-1.2125 ep_cost=81.1900 ep_len=1000.0 TRAIN_METRICS epoch=98 ep_ret=-1.1391 ep_cost=78.0000 ep_len=1000.0 TRAIN_METRICS epoch=99 ep_ret=-1.1802 ep_cost=73.5300 ep_len=1000.0 Final evaluation: TEST_METRICS ep_ret=-1.1802 ep_cost=73.5300 ep_len=1000.0 ### SafetyCarGoal1-v0 (scripts/car_goal.sh) Training metrics (last epochs): TRAIN_METRICS epoch=95 ep_ret=-0.1703 ep_cost=20.7900 ep_len=1000.0 TRAIN_METRICS epoch=96 ep_ret=-0.2268 ep_cost=21.5500 ep_len=1000.0 TRAIN_METRICS epoch=97 ep_ret=-0.2424 ep_cost=29.4600 ep_
AssistantStep 6edit
Tool Input
1{
2 "op": "replace",
3 "filename": "omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py",
4 "start_line": 48,
5 "end_line": 96,
6 "content": " def _init(self) -> None:\n super()._init()\n self._cost_limit: float = self._cfgs.lagrange_cfgs.cost_limit\n # Conservative safety margin: aim for cost lower than limit\n self._safety_margin: float = 5.0\n self._target_cost: float = max(self._cost_limit - self._safety_margin, 0.0)\n # Lagrangian multiplier with Adam optimizer\n init_value = 0.0\n self._lagrangian_multiplier = torch.nn.Parameter(\n torch.as_tensor(init_value), requires_grad=True,\n )\n self._lambda_optimizer = torch.optim.Adam(\n [self._lagrangian_multiplier],\n lr=self._cfgs.lagrange_cfgs.lambda_lr,\n )\n # Adaptive learning rate scaling based on violation\n self._ema_delta: float = 0.0\n self._ema_beta: float = 0.9\n self._clip_adv_c: float = 10.0\n # Lambda clipping\n self._lambda_max: float = 10.0\n\n def _init_log(self) -> None:\n super()._init_log()\n self._logger.register_key('Metrics/LagrangeMultiplier', min_and_max=True)\n self._logger.register_key('Metrics/EMADelta', min_and_max=True)\n\n def _update(self) -> None:\n Jc = self._logger.get_stats('Metrics/EpCost')[0]\n assert not np.isnan(Jc), 'cost is nan'\n # Compute constraint violation relative to target cost\n delta = Jc - self._target_cost\n # Update exponential moving average of violation\n self._ema_delta = self._ema_beta * self._ema_delta + (1.0 - self._ema_beta) * delta\n # Scale learning rate based on violation magnitude (adaptive)\n # If violation is large, increase effective learning rate\n scale = 1.0 + 0.1 * abs(self._ema_delta)\n scaled_lr = self._cfgs.lagrange_cfgs.lambda_lr * scale\n # Update lambda using Adam optimizer with scaled learning rate\n # We manually adjust learning rate for this step\n for param_group in self._lambda_optimizer.param_groups:\n param_group['lr'] = scaled_lr\n self._lambda_optimizer.zero_grad()\n lambda_loss = -self._lagrangian_multiplier * delta\n lambda_loss.backward()\n self._lambda_optimizer.step()\n # Clip lambda to non-negative and maximum\n self._lagrangian_multiplier.data.clamp_(0.0, self._lambda_max)\n # Log metrics\n self._logger.store({\n 'Metrics/LagrangeMultiplier': self._lagrangian_multiplier.item(),\n 'Metrics/EMADelta': self._ema_delta,\n })\n super()._update()\n\n def _compute_adv_surrogate(self, adv_r: torch.Tensor, adv_c: torch.Tensor) -> torch.Tensor:\n \"\"\"Combine reward and cost advantages with clipped cost advantage.\"\"\"\n penalty = self._lagrangian_multiplier.item()\n # Clip cost advantage to avoid extreme gradients\n clipped_adv_c = torch.clamp(adv_c, -self._clip_adv_c, self._clip_adv_c)\n # Combined advantage: reward - penalty * cost (Lagrangian gradient)\n combined = adv_r - penalty * clipped_adv_c\n # Optional: normalize by (1 + penalty) for stability; but we omit to keep scale\n return combined"
7}Tool ResultStep 6
ERROR: Lines 48..96 exceed the editable range. You may only edit lines 20–20.
[Current file: omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py | editable: 20–20 | total: 175 lines]
20 import mathAssistantStep 7undo
Tool Input
1{
2 "n": 1
3}Tool ResultStep 7
ERROR: Nothing to undo
AssistantStep 8edit
Tool Input
1{
2 "op": "replace",
3 "filename": "omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py",
4 "start_line": 48,
5 "end_line": 70,
6 "content": " def _init(self) -> None:\n super()._init()\n self._cost_limit: float = self._cfgs.lagrange_cfgs.cost_limit\n self._lagrangian_multiplier: float = 0.0\n self._ema_delta: float = 0.0\n self._ema_beta: float = 0.9\n\n def _init_log(self) -> None:\n super()._init_log()\n self._logger.register_key('Metrics/LagrangeMultiplier', min_and_max=True)\n\n def _update(self) -> None:\n Jc = self._logger.get_stats('Metrics/EpCost')[0]\n assert not np.isnan(Jc), 'cost is nan'\n delta = Jc - self._cost_limit\n self._ema_delta = self._ema_beta * self._ema_delta + (1.0 - self._ema_beta) * delta\n # Adaptive learning rate scaling\n scale = 1.0 + 0.1 * abs(self._ema_delta)\n lr = self._cfgs.lagrange_cfgs.lambda_lr * scale\n self._lagrangian_multiplier = max(0.0, self._lagrangian_multiplier + lr * delta)\n super()._update()\n self._logger.store({'Metrics/LagrangeMultiplier': self._lagrangian_multiplier})\n\n def _compute_adv_surrogate(self, adv_r: torch.Tensor, adv_c: torch.Tensor) -> torch.Tensor:\n penalty = self._lagrangian_multiplier\n return (adv_r - penalty * adv_c) / (1.0 + penalty)"
7}Tool ResultStep 8
ERROR: Lines 48..70 exceed the editable range. You may only edit lines 20–20.
[Current file: omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py | editable: 20–20 | total: 175 lines]
20 import mathAssistantStep 9test