Spaces:
Sleeping
Sleeping
File size: 6,851 Bytes
f0ca22d | 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 | # 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.
"""
Composable Rubric implementation of the RhythmEnv episode grader.
Mirrors the original `_grade_episode` in `rhythm_environment.py` but built
on top of `openenv.core.rubrics.Rubric` + `WeightedSum` — the framework's
official scoring composition primitives. Each Rubric subclass wraps one
of the 6 grader components; `make_rubric(env)` composes them with their
weights.
The `forward(action, observation)` signature is required by the Rubric
ABC. Because RhythmEnv grades at episode end (after `done=True`) using
aggregated env state — not per-(action, observation) data — these
subclasses ignore the per-step args and read directly from the env they
were constructed with. This is the recommended pattern from RFC 004 for
trajectory-summary scoring.
Used by `RhythmEnvironment._grade_episode`. The original numerical
implementation is preserved in the legacy code path; this file is the
primary, conformant implementation.
"""
from __future__ import annotations
from typing import Any, TYPE_CHECKING
from openenv.core.rubrics import Rubric, WeightedSum
if TYPE_CHECKING:
from server.rhythm_environment import RhythmEnvironment
# ---------------------------------------------------------------------------
# Component rubrics — one per scored axis of the final grade.
# ---------------------------------------------------------------------------
class CrashFreeRubric(Rubric):
"""Reward for keeping all 5 meters above the crash threshold.
Score = 1 − (crashes / total_possible_meter_step_drops). Higher is
better; perfect play (no meter ever drops below 0.10) gives 1.0.
"""
def __init__(self, env: "RhythmEnvironment") -> None:
super().__init__()
self._env = env
def forward(self, action: Any, observation: Any) -> float:
from server.rhythm_environment import METERS # local import avoids cycle
steps = max(self._env._timestep, 1)
return 1.0 - (self._env._crash_count / (steps * len(METERS)))
class ProgressRubric(Rubric):
"""Career/skill growth — final value of the progress meter."""
def __init__(self, env: "RhythmEnvironment") -> None:
super().__init__()
self._env = env
def forward(self, action: Any, observation: Any) -> float:
return float(self._env._progress)
class ConnectionRubric(Rubric):
"""Relationship maintenance — final value of the connection meter."""
def __init__(self, env: "RhythmEnvironment") -> None:
super().__init__()
self._env = env
def forward(self, action: Any, observation: Any) -> float:
return float(self._env._connection)
class AdaptationRubric(Rubric):
"""Implicit meta-learning signal: late-half mean reward minus early-half.
Scaled to [0, 1]. Per-step rewards are profile-weighted so a positive
gain means the agent is exploiting profile-aware play that it wasn't
using early. Gated by `late_quality` so a "terrible-then-mediocre"
exploit cannot win.
"""
def __init__(self, env: "RhythmEnvironment") -> None:
super().__init__()
self._env = env
def forward(self, action: Any, observation: Any) -> float:
steps = max(self._env._timestep, 1)
half = max(steps // 2, 1)
rewards = self._env._step_rewards
early = rewards[:half]
late = rewards[half:]
if not (early and late):
return 0.0
mean_early = sum(early) / len(early)
mean_late = sum(late) / len(late)
# Per-step rewards are clamped to [-3, +3] in step(), so normalize
# late_quality with the [-3, +3] range — without this, the gate
# saturates at 1.0 for any mean_late ≥ 1 and the grader can't
# distinguish good from excellent late-half quality.
late_quality = max(0.0, min(1.0, (mean_late + 3.0) / 6.0))
gain = mean_late - mean_early
# gain ∈ [-6, +6]; only positive gain counts, normalized to [0, 1]
gain_norm = max(0.0, min(1.0, gain / 3.0))
return gain_norm * late_quality
class EfficiencyRubric(Rubric):
"""Bounded normalized average per-step reward across the episode."""
def __init__(self, env: "RhythmEnvironment") -> None:
super().__init__()
self._env = env
def forward(self, action: Any, observation: Any) -> float:
steps = max(self._env._timestep, 1)
avg_reward = self._env._total_reward / steps
return max(0.0, min(1.0, (avg_reward + 1.0) / 2.0))
class BeliefAccuracyRubric(Rubric):
"""Explicit meta-RL inference signal.
Score = max(0, 1 − MAE) between the agent's last-emitted belief and
the true profile vector. Returns 0 if the agent never emitted a
belief (heuristic / random baselines) — by design, only agents that
actually try to infer get credit on this axis.
"""
def __init__(self, env: "RhythmEnvironment") -> None:
super().__init__()
self._env = env
def forward(self, action: Any, observation: Any) -> float:
from server.rhythm_environment import profile_to_belief_vector
emitted = self._env._final_belief
if emitted is None:
return 0.0
true_belief = profile_to_belief_vector(self._env._profile)
mae = sum(abs(b - t) for b, t in zip(emitted, true_belief)) / 3.0
return max(0.0, 1.0 - mae)
# ---------------------------------------------------------------------------
# Composition
# ---------------------------------------------------------------------------
# Weights matching the original _grade_episode formula; sum to 1.0.
GRADE_WEIGHTS = {
"crash_free": 0.15,
"progress": 0.20,
"connection": 0.10,
"adaptation": 0.25,
"efficiency": 0.10,
"belief_accuracy": 0.20,
}
def make_grade_rubric(env: "RhythmEnvironment") -> WeightedSum:
"""Build the composed `WeightedSum` rubric for grading episodes.
Returns a single `Rubric` whose `forward(None, None)` reads the env's
aggregated state and returns the same final_score the original
`_grade_episode` would have computed.
"""
return WeightedSum(
rubrics=[
CrashFreeRubric(env),
ProgressRubric(env),
ConnectionRubric(env),
AdaptationRubric(env),
EfficiencyRubric(env),
BeliefAccuracyRubric(env),
],
weights=[
GRADE_WEIGHTS["crash_free"],
GRADE_WEIGHTS["progress"],
GRADE_WEIGHTS["connection"],
GRADE_WEIGHTS["adaptation"],
GRADE_WEIGHTS["efficiency"],
GRADE_WEIGHTS["belief_accuracy"],
],
)
|