Spaces:
Sleeping
Sleeping
File size: 1,897 Bytes
678aaed a5fc2da 678aaed a5fc2da 678aaed a5fc2da 678aaed a5fc2da 678aaed a5fc2da 678aaed a5fc2da 678aaed | 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 | # 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.
"""Skill Forge Environment Client."""
from typing import Dict
from openenv.core.client_types import StepResult
from openenv.core.env_server.types import State
from openenv.core import EnvClient
from .models import SkillForgeAction, SkillForgeObservation
class SkillForgeEnv(
EnvClient[SkillForgeAction, SkillForgeObservation, State]
):
"""
Client for the Skill Forge Environment.
Maintains a persistent WebSocket connection to the environment server.
Each client instance has its own dedicated environment session.
Example:
>>> with SkillForgeEnv(base_url="http://localhost:8000") as client:
... result = client.reset()
... print(result.observation.task_description)
...
... action = SkillForgeAction(
... action_type="raw_code",
... content="df.sort_values('revenue', ascending=False)['product'].tolist()",
... )
... result = client.step(action)
... print(result.observation.result_correct)
"""
def _step_payload(self, action: SkillForgeAction) -> Dict:
return action.model_dump()
def _parse_result(self, payload: Dict) -> StepResult[SkillForgeObservation]:
obs_data = payload.get("observation", {})
observation = SkillForgeObservation(**obs_data)
return StepResult(
observation=observation,
reward=payload.get("reward"),
done=payload.get("done", False),
)
def _parse_state(self, payload: Dict) -> State:
return State(
episode_id=payload.get("episode_id"),
step_count=payload.get("step_count", 0),
)
|