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7c07390 | 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 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | class Environment(ABC, Generic[ActT, ObsT, StateT]):
"""Base class for all environment servers following Gym/Gymnasium API.
Args:
transform: Optional transform to apply to observations
rubric: Optional rubric for reward computation. When provided, the
rubric's output can be used to set the observation's reward in step().
Class Attributes:
SUPPORTS_CONCURRENT_SESSIONS: Whether this environment supports concurrent sessions.
When True, multiple WebSocket connections can each have their own
environment instance (up to max_concurrent_envs). When False (default),
the environment should only be used with a single session at a time.
Set this to True in your Environment subclass if:
- The environment uses proper session isolation (e.g., unique working dirs)
- No shared mutable state exists between instances
- External resources (databases, APIs) can handle concurrent access
Attributes:
rubric: Optional rubric for computing rewards. Environments can set this
in __init__ and use it in step() to compute observation rewards.
Training infrastructure can access it for introspection:
for name, r in env.rubric.named_rubrics():
print(f"{name}: {r.last_score}")
See RFC 004 for rubric design: rfcs/004-rubrics.md
"""
# Class-level flag indicating whether this environment supports concurrent sessions
SUPPORTS_CONCURRENT_SESSIONS: bool = False
# Optional rubric for reward computation
rubric: Optional["Rubric"]
def __init__(
self,
transform: Optional[Transform[ObsT]] = None,
rubric: Optional["Rubric"] = None,
):
self.transform = transform
self.rubric = rubric
@abstractmethod
def reset(
self,
seed: Optional[int] = None,
episode_id: Optional[str] = None,
**kwargs: Any,
) -> ObsT:
"""Reset the environment and return initial observation."""
pass
async def reset_async(
self,
seed: Optional[int] = None,
episode_id: Optional[str] = None,
**kwargs: Any,
) -> ObsT:
"""Async version of reset. Default implementation calls sync reset.
Override to provide true async implementation.
"""
return self.reset(seed=seed, episode_id=episode_id, **kwargs)
@abstractmethod
def step(
self,
action: ActT,
timeout_s: Optional[float] = None,
**kwargs: Any,
) -> ObsT:
"""Take a step in the environment."""
pass
async def step_async(
self,
action: ActT,
timeout_s: Optional[float] = None,
**kwargs: Any,
) -> ObsT:
"""Async version of step. Default implementation calls sync step.
Override to provide true async implementation.
"""
return self.step(action, timeout_s=timeout_s, **kwargs)
@property
@abstractmethod
def state(self) -> StateT:
"""Get the current environment state."""
pass
def get_metadata(self) -> EnvironmentMetadata:
"""
Get metadata about this environment.
Override this method to provide custom metadata for the environment.
Default implementation returns basic metadata derived from class name.
Returns:
EnvironmentMetadata with environment information
"""
return EnvironmentMetadata(
name=self.__class__.__name__,
description=f"{self.__class__.__name__} environment",
version="1.0.0",
)
def _apply_transform(self, observation: ObsT) -> ObsT:
"""Apply transform if one is provided."""
if self.transform is not None:
return self.transform(observation)
return observation
def _apply_rubric(self, action: ActT, observation: ObsT) -> float:
"""Apply rubric if one is provided.
Args:
action: The action taken by the agent.
observation: The resulting observation.
Returns:
Reward value from the rubric, or 0.0 if no rubric is set.
Usage in step():
def step(self, action: MyAction, ...) -> MyObservation:
# ... execute action and create observation ...
observation.reward = self._apply_rubric(action, observation)
return observation
"""
if self.rubric is not None:
return self.rubric(action, observation)
return 0.0
async def _apply_rubric_async(self, action: ActT, observation: ObsT) -> float:
"""Apply rubric asynchronously if one is provided.
Args:
action: The action taken by the agent.
observation: The resulting observation.
Returns:
Reward value from the rubric, or 0.0 if no rubric is set.
Usage in step_async():
async def step_async(self, action: MyAction, ...) -> MyObservation:
# ... execute action and create observation ...
observation.reward = await self._apply_rubric_async(action, observation)
return observation
"""
if self.rubric is not None:
result = self.rubric(action, observation)
# If rubric returns a coroutine, await it
if inspect.iscoroutine(result):
return await result
return result
return 0.0
def _reset_rubric(self) -> None:
"""Reset the rubric state if one is provided.
Call this in reset() to clear any trajectory state in the rubric.
Usage in reset():
def reset(self, ...) -> MyObservation:
self._reset_rubric()
# ... create initial observation ...
return observation
"""
if self.rubric is not None:
self.rubric.reset()
async def _reset_rubric_async(self) -> None:
"""Reset the rubric state asynchronously if one is provided.
Call this in reset_async() to clear any trajectory state in the rubric.
Usage in reset_async():
async def reset_async(self, ...) -> MyObservation:
await self._reset_rubric_async()
# ... create initial observation ...
return observation
"""
if self.rubric is not None:
# Check if rubric has async reset method
if hasattr(self.rubric, "reset_async"):
result = self.rubric.reset_async()
if inspect.iscoroutine(result):
await result
else:
self.rubric.reset()
def close(self) -> None:
"""Clean up resources used by the environment.
Override this method to implement custom cleanup logic.
Called when the environment is being destroyed or reset.
"""
pass
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