Spaces:
Sleeping
Sleeping
File size: 6,607 Bytes
d9175ae | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 | import random
import uuid
from openenv.core.env_server import Environment
from tool_use_env.models import ToolUseAction, ToolUseObservation, ToolUseState
from tool_use_env.grader import compute_grade
class ToolUseEnvironment(Environment):
SUPPORTS_CONCURRENT_SESSIONS = True
def __init__(self):
self._state = ToolUseState()
self._tasks = self._load_tasks()
def _load_tasks(self):
return [
{
"query": "What is 5 + 7?",
"answer": "12",
"correct_action": "use_calculator",
"difficulty": "easy"
},
{
"query": "Capital of France?",
"answer": "Paris",
"correct_action": "use_search",
"difficulty": "easy"
},
{
"query": "What is 123 * 456?",
"answer": "56088",
"correct_action": "use_calculator",
"difficulty": "hard"
},
{
"query": "What is 25 * 4?",
"answer": "100",
"correct_action": "use_calculator",
"difficulty": "medium"
},
{
"query": "Who is the CEO of Tesla?",
"answer": "Elon Musk",
"correct_action": "use_search",
"difficulty": "medium"
}
]
def reset(self, seed=None, episode_id=None, **kwargs) -> ToolUseObservation:
task = random.choice(self._tasks)
self._state = ToolUseState(
episode_id=episode_id or str(uuid.uuid4()),
step_count=0,
current_query=task["query"],
correct_action=task["correct_action"],
correct_answer=task["answer"],
difficulty=task["difficulty"]
)
return ToolUseObservation(
done=False,
reward=None,
query=task["query"],
tool_output=None,
message="Choose an action"
)
# 🔢 Calculator tool (controlled noise)
def _calculator(self, query):
try:
expr = query.lower()
expr = expr.replace("what is", "").replace("?", "").strip()
correct = eval(expr)
difficulty = self._state.difficulty
if difficulty == "easy":
fail_prob = 0.06
elif difficulty == "medium":
fail_prob = 0.12
else:
fail_prob = 0.18
# complexity-based failure
if len(query) > 20:
fail_prob += 0.05
# 🔥 cap failure (IMPORTANT)
fail_prob = min(fail_prob, 0.25)
if random.random() < fail_prob:
# 🔥 scale noise based on magnitude
if abs(correct) < 50:
noise = random.randint(-2, 2)
else:
noise = int(correct * random.uniform(-0.05, 0.05))
return str(correct + noise)
return str(correct)
except Exception:
return "error"
# 🔍 Search tool (controlled noise)
def _search(self, query):
kb = {
"Capital of France": "Paris",
"CEO of Tesla": "Elon Musk"
}
difficulty = self._state.difficulty
for key in kb:
if key.lower() in query.lower():
if difficulty == "easy":
fail_prob = 0.07
elif difficulty == "medium":
fail_prob = 0.15
else:
fail_prob = 0.22
# complexity-based failure
if len(query) > 20:
fail_prob += 0.05
# 🔥 cap failure
fail_prob = min(fail_prob, 0.30)
if random.random() < fail_prob:
return random.choice([
"Unknown",
"Not sure",
"No results found"
])
return kb[key]
return "not found"
def step(self, action: ToolUseAction, timeout_s=None, **kwargs) -> ToolUseObservation:
self._state.step_count += 1
query = self._state.current_query
correct_action = self._state.correct_action
correct_answer = self._state.correct_answer
difficulty = self._state.difficulty
action_type = action.action_type
# --- Execute tool ---
if action_type == "use_calculator":
output = self._calculator(query)
elif action_type == "use_search":
output = self._search(query)
elif action_type == "answer_directly":
output = "unknown"
else:
output = "invalid action"
# --- Check correctness ---
answer_correct = (output == correct_answer)
# 🧠 REWARD SYSTEM (FINAL)
# 1. Action correctness
action_score = 0.4 if action_type == correct_action else 0.1
# 2. Answer correctness
answer_score = 0.5 if answer_correct else 0.0
# 3. Tool cost (small penalty)
if action_type == "use_calculator":
tool_penalty = 0.05
elif action_type == "use_search":
tool_penalty = 0.08
else:
tool_penalty = 0.0
# 4. Failure bonus (good reasoning but tool failed)
failure_bonus = 0.1 if (not answer_correct and action_type == correct_action) else 0.0
# 5. Combine
reward = action_score + answer_score + failure_bonus - tool_penalty
# 6. Difficulty scaling (light)
if difficulty == "medium":
reward *= 1.02
elif difficulty == "hard":
reward *= 0.9
# 7. Clamp (VERY IMPORTANT)
reward = max(0.0, min(1.0, reward))
# --- Grade (for reporting only) ---
grade = compute_grade(
action_taken=action_type,
correct_action=correct_action,
output=output,
correct_answer=correct_answer
)
return ToolUseObservation(
done=True,
reward=reward,
query=query,
tool_output=output,
message=(
f"Action: {action_type}, "
f"Output: {output}, "
f"Correct: {answer_correct}, "
f"Reward: {reward:.2f}, "
f"Grade: {grade:.2f}"
)
)
@property
def state(self) -> ToolUseState:
return self._state |