Agent Conversation: gpt-5.4

Model: gpt-5.4

Total steps: 5

Messages: 13

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. _upda

AssistantStep 1edit

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._lambda_lr: float = self._cfgs.lagrange_cfgs.lambda_lr\n        # Novel mechanism: optimistic-conservative mirror multiplier with violation momentum\n        self._lagrangian_multiplier: float = 0.0\n        self._violation_ema: float = 0.0\n        self._feasible_ema: float = 0.0\n        self._cost_var_ema: float = 0.0\n        self._prev_delta: float = 0.0\n        self._ema_beta: float = 0.9\n        self._var_beta: float = 0.95\n        self._damp_beta: float = 0.5\n        self._safety_margin: float = 0.10 * self._cost_limit\n        self._lambda_cap: 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\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 = float(Jc - self._cost_limit)\n        # Track both violation pressure and feasibility confidence.\n        pos_delta = max(delta, 0.0)\n        neg_delta = max(-delta, 0.0)\n        self._violation_ema = self._ema_beta * self._violation_ema + (1 - self._ema_beta) * pos_delta\n        self._feasible_ema = self._ema_beta * self._feasible_ema + (1 - self._ema_beta) * neg_delta\n        centered = delta - self._prev_delta\n        self._cost_var_ema = self._var_beta * self._cost_var_ema + (1 - self._var_beta) * (delta * delta)\n        risk_scale = float(np.sqrt(self._cost_var_ema + 1e-8))\n        # Update target combines current violation, trend, and a variance-adaptive safety margin.\n        trend = centered\n        target = (\n            self._violation_ema\n            + 0.5 * pos_delta\n            + 0.25 * max(trend, 0.0)\n            - 0.15 * self._feasible_ema\n            - 0.05 * max(-trend, 0.0)\n            + 0.1 * max(risk_scale - self._safety_margin, 0.0)\n        )\n        target = max(0.0, target / (self._cost_limit + 1e-8))\n        # Mirror-style interpolation: rise fast when unsafe, decay slowly when safe.\n        if delta > 0.0:\n            step = self._lambda_lr * (1.0 + target)\n        else:\n            step = self._lambda_lr * 0.25\n        self._lagrangian_multiplier = (1 - step) * self._lagrangian_multiplier + step * target\n        self._lagrangian_multiplier = float(np.clip(self._lagrangian_multiplier, 0.0, self._lambda_cap))\n        self._prev_delta = 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        \"\"\"Variance-aware gated reward-cost advantage coupling.\"\"\"\n        penalty = float(self._lagrangian_multiplier)\n        adv_c_center = adv_c - adv_c.mean()\n        cost_scale = adv_c_center.abs().mean().detach() + 1e-6\n        reward_scale = adv_r.abs().mean().detach() + 1e-6\n        normalized_cost = adv_c_center / cost_scale\n        # Gate sharpens only when multiplier is active, preserving reward learning in feasible regions.\n        gate = torch.sigmoid(normalized_cost * penalty)\n        adaptive_penalty = penalty * (0.5 + gate)\n        return (adv_r / reward_scale - adaptive_penalty * normalized_cost) / (1.0 + penalty)"
7}

Tool ResultStep 1

OK: Replaced lines 48..70 with 65 line(s) in omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py. Editable range: 20–20, 48–112.

[Current file: omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py | editable: 20–20, 48–112 | total: 191 lines]
    20  
...
    48      def _init(self) -> None:
    49          super()._init()
    50          self._cost_limit: float = self._cfgs.lagrange_cfgs.cost_limit
       ... (59 more lines) ...
   110          gate = torch.sigmoid(normalized_cost * penalty)
   111          adaptive_penalty = penalty * (0.5 + gate)
   112          return (adv_r / reward_scale - adaptive_penalty * normalized_cost) / (1.0 + penalty)

AssistantStep 2test

Tool ResultStep 2

[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=91 ep_ret=18.5372 ep_cost=49.6400 ep_len=1000.0
TRAIN_METRICS epoch=92 ep_ret=18.7751 ep_cost=52.1400 ep_len=1000.0
TRAIN_METRICS epoch=93 ep_ret=18.9717 ep_cost=55.6400 ep_len=1000.0
TRAIN_METRICS epoch=94 ep_ret=19.1327 ep_cost=53.4600 ep_len=1000.0
TRAIN_METRICS epoch=95 ep_ret=19.4145 ep_cost=51.7200 ep_len=1000.0

### SafetyCarGoal1-v0 (scripts/car_goal.sh)
Training metrics (last epochs):
TRAIN_METRICS epoch=94 ep_ret=14.5266 ep_cost=51.6900 ep_len=1000.0
TRAIN_METRICS epoch=95 ep_ret=14.6518 ep_cost=53.5500 ep_len=1000.0
TRAIN_METRICS epoch=96 ep_ret=14.6068 ep_cost=56.2000 ep_len=1000.0
TRAIN_METRICS epoch=97 ep_ret=14.6224 ep_cost=54.3700 ep_len=1000.0
TRAIN_METRICS epoch=98 ep_ret=15.2221 ep_cost=56.5200 ep_len=1000.0

### SafetyPointButton1-v0 (scripts/point_button.sh)
T

AssistantStep 3edit

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": 112,
6  "content": "    def _init(self) -> None:\n        super()._init()\n        self._cost_limit: float = self._cfgs.lagrange_cfgs.cost_limit\n        self._lambda_lr: float = self._cfgs.lagrange_cfgs.lambda_lr\n        # Novel mechanism: predictive integral barrier with safety-budget normalization.\n        self._lagrangian_multiplier: float = 0.0\n        self._ema_delta: float = 0.0\n        self._ema_abs_delta: float = 0.0\n        self._integral_violation: float = 0.0\n        self._prev_cost: float = self._cost_limit\n        self._ema_beta: float = 0.9\n        self._lambda_cap: float = 20.0\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\n        delta = float(Jc - self._cost_limit)\n        norm_delta = delta / (self._cost_limit + 1e-8)\n        cost_velocity = float(Jc - self._prev_cost) / (self._cost_limit + 1e-8)\n\n        self._ema_delta = self._ema_beta * self._ema_delta + (1.0 - self._ema_beta) * norm_delta\n        self._ema_abs_delta = self._ema_beta * self._ema_abs_delta + (1.0 - self._ema_beta) * abs(norm_delta)\n        self._integral_violation = max(0.0, self._integral_violation + self._ema_delta)\n\n        # Predictive barrier target: current violation + upward trend + persistent excess cost.\n        pred_violation = self._ema_delta + 0.5 * max(cost_velocity, 0.0)\n        raw_target = max(0.0, pred_violation) + 0.5 * self._integral_violation\n        # If persistently feasible, actively release the multiplier.\n        if norm_delta < 0.0:\n            raw_target -= 0.25 * abs(norm_delta)\n        target = float(np.clip(raw_target, 0.0, self._lambda_cap))\n\n        # Aggressive rise when unsafe, conservative decay when safe.\n        rise_rate = min(1.0, self._lambda_lr * (2.0 + 4.0 * max(norm_delta, 0.0)))\n        decay_rate = min(1.0, self._lambda_lr * 0.5)\n        mix = rise_rate if target > self._lagrangian_multiplier else decay_rate\n        self._lagrangian_multiplier = (1.0 - mix) * self._lagrangian_multiplier + mix * target\n        self._lagrangian_multiplier = float(np.clip(self._lagrangian_multiplier, 0.0, self._lambda_cap))\n        self._prev_cost = float(Jc)\n\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        \"\"\"Budget-normalized adaptive penalization with one-sided cost emphasis.\"\"\"\n        penalty = float(self._lagrangian_multiplier)\n        adv_c_center = adv_c - adv_c.mean()\n        adv_c_scale = adv_c_center.abs().mean().detach() + 1e-6\n        normalized_cost = adv_c_center / adv_c_scale\n        # Penalize above-average cost actions more strongly than below-average ones.\n        risky_cost = torch.relu(normalized_cost) + 0.25 * torch.minimum(\n            normalized_cost,\n            torch.zeros_like(normalized_cost),\n        )\n        return (adv_r - penalty * risky_cost) / (1.0 + penalty)"
7}

Tool ResultStep 3

OK: Replaced lines 48..112 with 60 line(s) in omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py. Editable range: 20–20, 48–107.

[Current file: omnisafe/omnisafe/algorithms/on_policy/naive_lagrange/custom_lag.py | editable: 20–20, 48–107 | total: 186 lines]
    20  
...
    48      def _init(self) -> None:
    49          super()._init()
    50          self._cost_limit: float = self._cfgs.lagrange_cfgs.cost_limit
       ... (54 more lines) ...
   105              torch.zeros_like(normalized_cost),
   106          )
   107          return (adv_r - penalty * risky_cost) / (1.0 + penalty)

AssistantStep 4test

Tool ResultStep 4

[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=90 ep_ret=-0.0481 ep_cost=5.1400 ep_len=1000.0
TRAIN_METRICS epoch=91 ep_ret=-0.0740 ep_cost=4.7300 ep_len=1000.0
TRAIN_METRICS epoch=92 ep_ret=-0.0022 ep_cost=7.6500 ep_len=1000.0
TRAIN_METRICS epoch=93 ep_ret=0.0948 ep_cost=5.0200 ep_len=1000.0
TRAIN_METRICS epoch=94 ep_ret=0.3729 ep_cost=6.8000 ep_len=1000.0

### SafetyCarGoal1-v0 (scripts/car_goal.sh)
Training metrics (last epochs):
TRAIN_METRICS epoch=95 ep_ret=0.4582 ep_cost=8.5000 ep_len=1000.0
TRAIN_METRICS epoch=96 ep_ret=0.4625 ep_cost=9.8800 ep_len=1000.0
TRAIN_METRICS epoch=97 ep_ret=0.4391 ep_cost=7.6400 ep_len=1000.0
TRAIN_METRICS epoch=98 ep_ret=0.2665 ep_cost=8.5100 ep_len=1000.0
TRAIN_METRICS

AssistantStep 5submit

Tool Input

1{
2  "n": 2
3}

Tool ResultStep 5

[submit] Submitting result from test #2 as final.


## Seed 42

### SafetyPointGoal1-v0 (scripts/point_goal.sh)
Training metrics (last epochs):
TRAIN_METRICS epoch=90 ep_ret=-0.0481 ep_cost=5.1400 ep_len=1000.0
TRAIN_METRICS epoch=91 ep_ret=-0.0740 ep_cost=4.7300 ep_len=1000.0
TRAIN_METRICS epoch=92 ep_ret=-0.0022 ep_cost=7.6500 ep_len=1000.0
TRAIN_METRICS epoch=93 ep_ret=0.0948 ep_cost=5.0200 ep_len=1000.0
TRAIN_METRICS epoch=94 ep_ret=0.3729 ep_cost=6.8000 ep_len=1000.0

### SafetyCarGoal1-v0 (scripts/car_goal.sh)
Training metrics (last epochs):
TRAIN_METRICS epoch=95 ep_ret=0.4582 ep_cost=8.5000 ep_len=1000.0
TRAIN_METRICS epoch=96 ep_ret=0.4625 ep_cost=9.8800 ep_len=1000.0
TRAIN_METRICS epoch=97 ep_ret=0.4391 ep_cost=7.6400 ep_len=1000.0
TRAIN_METRICS epoch=98 ep_ret=0.2665 ep_cost=8.5100 ep_len=1000.0
TRAIN_METRICS epoch=99 ep_ret=0.2395 ep_cost=8.1500 ep_len=1000.0
Final evaluation:
  TEST_METRICS ep_ret=0.2395 ep_cost=8.1500 ep_len=1000.0

### SafetyPointButton1-v0 (scripts/poin