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Configuration module for OpenEnv Email Triage environment parameters.
"""
from pydantic import BaseModel, Field
from typing import Optional, Dict, Any, Tuple
import json
class EnvConfig(BaseModel):
"""
Configuration class for OpenEnv Email Triage environment.
"""
# Task difficulty
task_level: str = Field(default="medium", description="Task level: 'easy', 'medium', or 'hard'")
# Environment dynamics
num_emails: int = Field(default=20, description="Total number of emails to triage")
spam_ratio: float = Field(default=0.3, description="Ratio of emails that are spam")
urgent_ratio: float = Field(default=0.2, description="Ratio of emails that are urgent")
confounding_ratio: float = Field(default=0.1, description="Ratio of confusing/nuanced emails (medium/hard only)")
# Reward configuration
reward_scale: float = Field(default=1.0)
# Rendering options
render_mode: Optional[str] = Field(default=None)
render_fps: int = Field(default=60)
screen_size: Tuple[int, int] = Field(default=(1024, 768))
# Logging
verbose: bool = Field(default=True)
random_seed: Optional[int] = Field(default=None)
custom_params: Dict[str, Any] = Field(default_factory=dict)
def validate(self) -> bool:
if self.num_emails <= 0:
raise ValueError("num_emails must be positive")
if not (0.0 <= self.spam_ratio <= 1.0):
raise ValueError("spam_ratio must be between 0 and 1")
if self.task_level not in ["easy", "medium", "hard"]:
raise ValueError(f"Unknown task level: {self.task_level}")
return True
def to_dict(self) -> Dict[str, Any]:
return self.model_dump()
@classmethod
def from_dict(cls, config_dict: Dict[str, Any]) -> 'EnvConfig':
return cls(**config_dict)
def save(self, filepath: str) -> None:
with open(filepath, 'w') as f:
json.dump(self.to_dict(), f, indent=2)
@staticmethod
def load(filepath: str) -> 'EnvConfig':
with open(filepath, 'r') as f:
config_dict = json.load(f)
return EnvConfig.from_dict(config_dict)
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