Upload alea/envs/base.py with huggingface_hub

alea/envs/base.py

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+"""DecisionEnv protocol for Alea RL environments.
+
+Every env produces a stream of DECISION POINTS. At each one it emits a
+(state_text, questions) observation in Alea's packed-request format, the
+policy answers, and the env scores it.
+
+Two reward modes, env-dependent:
+  - "dense": env.action_rewards() returns an exact per-action reward vector
+    (regret-normalized so the optimal action = 1.0). GRPO samples k actions
+    and scores each exactly — no rollout branching needed.
+  - "outcome": env.step() returns only the realized scalar reward; terminal
+    returns are assigned to every decision in the episode (GRPO over k
+    full-episode trajectories via clone()).
+
+An env may also emit REPORT questions (score/regress/noul with exact
+targets — e.g., "what is your belief over hidden states?") alongside the
+action question. Report targets come from oracle_targets() and are scored
+with the supervised proper-scoring losses — a variance-free calibration
+signal no generative model can match.
+"""
+from __future__ import annotations
+
+import copy
+from dataclasses import dataclass
+
+from ..packing import QuestionSpec
+
+ACT_QID = "act"
+
+
+@dataclass
+class Decision:
+    """One decision point."""
+    state: str                      # text/JSON the model reads
+    questions: list                 # [QuestionSpec]; must contain qid=ACT_QID
+    n_legal: int                    # how many leading options are legal
+
+
+class DecisionEnv:
+    """Base class. Subclasses implement reset/observe/step."""
+    name = "base"
+    dense = False                   # True -> action_rewards() available
+
+    def reset(self, rng):
+        raise NotImplementedError
+
+    def observe(self) -> Decision:
+        """Current state + questions. questions[0] must be the action
+        question (qid=ACT_QID); its options are the action labels."""
+        raise NotImplementedError
+
+    def action_rewards(self):
+        """Dense mode only: exact reward per legal action, normalized so
+        max == 1.0 (optimal). Return None if unavailable."""
+        return None
+
+    def oracle_targets(self) -> dict:
+        """Exact targets for REPORT questions (qid -> target), and optionally
+        the optimal action distribution under key ACT_QID."""
+        return {}
+
+    def step(self, action_idx: int):
+        """Apply action -> (reward, done)."""
+        raise NotImplementedError
+
+    def clone(self):
+        return copy.deepcopy(self)
+
+    @property
+    def done(self):
+        return self._done