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4bd5225 | 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 | from __future__ import annotations
import itertools
import numpy as np
from .config import BenchmarkConfig
def decode_continuous_action(raw_action: np.ndarray, config: BenchmarkConfig) -> np.ndarray:
market = config.market
action = np.nan_to_num(np.asarray(raw_action, dtype=np.float32), nan=0.0, posinf=1.0, neginf=0.0)
action = np.clip(action, 0.0, 1.0)
reward_ratio = market.min_reward_ratio + action[0] * (market.max_reward_ratio - market.min_reward_ratio)
liquidity_ratio = market.min_liquidity_ratio + action[1] * (market.max_liquidity_ratio - market.min_liquidity_ratio)
burn_rate = action[2] * market.max_burn_rate
return sanitize_actual_action(np.array([reward_ratio, liquidity_ratio, burn_rate], dtype=np.float32), config)
def encode_actual_action(actual_action: np.ndarray, config: BenchmarkConfig) -> np.ndarray:
market = config.market
reward, liquidity, burn = sanitize_actual_action(actual_action, config)
return np.array(
[
(reward - market.min_reward_ratio) / (market.max_reward_ratio - market.min_reward_ratio),
(liquidity - market.min_liquidity_ratio) / (market.max_liquidity_ratio - market.min_liquidity_ratio),
burn / market.max_burn_rate,
],
dtype=np.float32,
).clip(0.0, 1.0)
def sanitize_actual_action(actual_action: np.ndarray, config: BenchmarkConfig) -> np.ndarray:
market = config.market
reward_ratio = float(np.clip(actual_action[0], market.min_reward_ratio, market.max_reward_ratio))
liquidity_ratio = float(np.clip(actual_action[1], market.min_liquidity_ratio, market.max_liquidity_ratio))
burn_rate = float(np.clip(actual_action[2], 0.0, market.max_burn_rate))
total = reward_ratio + liquidity_ratio
if total > market.max_total_allocation:
scale = market.max_total_allocation / total
reward_ratio *= scale
liquidity_ratio *= scale
return np.array([reward_ratio, liquidity_ratio, burn_rate], dtype=np.float32)
def discrete_action_grid(config: BenchmarkConfig) -> np.ndarray:
values = np.linspace(0.0, 1.0, int(config.discrete_levels), dtype=np.float32)
return np.array(list(itertools.product(values, values, values)), dtype=np.float32)
def decode_discrete_action(action_index: int, config: BenchmarkConfig) -> np.ndarray:
grid = discrete_action_grid(config)
index = int(np.clip(action_index, 0, len(grid) - 1))
return decode_continuous_action(grid[index], config)
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