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dd8b2b6 5371126 dd8b2b6 c13be93 0ad3f8a c13be93 dd8b2b6 9937e76 e0b43d0 9937e76 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
Data models for the Edupilot Environment.
The EduPilot environment is a simple test environment that echoes back messages.
"""
from openenv.core.env_server.types import Action, Observation
from pydantic import BaseModel, Field
class EdupilotAction(Action):
"""Action for the Edupilot environment - just a message to echo."""
message: str = Field(..., description="Message to echo back")
class EdupilotObservation(Observation):
"""Observation from the Edupilot environment - the echoed message."""
echoed_message: str = Field(default="", description="The echoed message")
message_length: int = Field(default=0, description="Length of the echoed message")
goal: str = Field(
default="Create a reminder email notification object in JSON format exactly matching the required schema and expected values",
description="Goal of the agent to perform",
)
last_action_error: bool = Field(
default=False, description="whether there was last action error"
)
reward_observations: list = Field(
default=[], description="Observations for what was rewarded and by what"
)
success_ratio: float = Field(
default=0.0, description="Success ratio against the benchmark"
)
mean_performance: float = Field(
default=0.0, description="Mean performance of the agent"
)
class EdupilotRewards(BaseModel):
total_reward: float = Field(default=0.0, description="Total rewards collected")
reward_observations: list = Field(default={}, description="Observations for what was rewarded and by what")
class EdupilotMetrics(BaseModel):
benchmark: float = Field(
default=0.0, description="Benchmark to evaluate results against"
)
current_reward: float = Field(default=0.0, description="Current total reward")
success_ratio: float = Field(
default=0.0, description="Success ratio against the benchmark"
)
mean_performance: float = Field(
default=0.0, description="Mean performance of the agent/model"
)
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