feature(xjy): add multi-task learning pipeline in jericho environment

.gitignore

@@ -1450,4 +1450,4 @@ events.*
 !/assets/pooltool/**
 lzero/mcts/ctree/ctree_alphazero/pybind11
 
-zoo/jericho/envs/z-machine-games-master
+zoo/jericho/envs/z-machine-games-master

lzero/entry/__init__.py

@@ -9,4 +9,9 @@
 from .train_rezero import train_rezero
 from .train_unizero import train_unizero
 from .train_unizero_segment import train_unizero_segment
+from .train_muzero_multitask_segment_ddp import train_muzero_multitask_segment_ddp
+from .train_unizero_multitask_segment_ddp import train_unizero_multitask_segment_ddp
+from .train_unizero_multitask_segment_eval import train_unizero_multitask_segment_eval
+from .train_unizero_multitask_ddp import train_unizero_multitask_ddp
+from .train_unizero_multitask import train_unizero_multitask
 from .utils import *

lzero/entry/compute_task_weight.py

@@ -0,0 +1,80 @@
+
+
+
+import numpy as np
+import torch
+
+
+def symlog(x: torch.Tensor) -> torch.Tensor:
+    """
+    Symlog 归一化，减少目标值的幅度差异。
+    symlog(x) = sign(x) * log(|x| + 1)
+    """
+    return torch.sign(x) * torch.log(torch.abs(x) + 1)
+
+
+def inv_symlog(x: torch.Tensor) -> torch.Tensor:
+    """
+    Symlog 的逆操作，用于恢复原始值。
+    inv_symlog(x) = sign(x) * (exp(|x|) - 1)
+    """
+    return torch.sign(x) * (torch.exp(torch.abs(x)) - 1)
+
+
+def compute_task_weights(
+    task_rewards: dict,
+    epsilon: float = 1e-6,
+    min_weight: float = 0.1,
+    max_weight: float = 0.5,
+    temperature: float = 1.0,
+    use_symlog: bool = True,
+) -> dict:
+    """
+    改进后的任务权重计算函数，加入 symlog 处理和鲁棒性设计。
+
+    Args:
+        task_rewards (dict): 每个任务的字典，键为 task_id，值为评估奖励。
+        epsilon (float): 避免分母为零的小值。
+        min_weight (float): 权重的最小值，用于裁剪。
+        max_weight (float): 权重的最大值，用于裁剪。
+        temperature (float): 控制权重分布的温度系数。
+        use_symlog (bool): 是否使用 symlog 对 task_rewards 进行矫正。
+
+    Returns:
+        dict: 每个任务的权重，键为 task_id，值为归一化并裁剪后的权重。
+    """
+    # Step 1: 矫正奖励值（可选，使用 symlog）
+    if use_symlog:
+        rewards_tensor = torch.tensor(list(task_rewards.values()), dtype=torch.float32)
+        corrected_rewards = symlog(rewards_tensor).numpy()  # 使用 symlog 矫正
+        task_rewards = dict(zip(task_rewards.keys(), corrected_rewards))
+
+    # Step 2: 计算初始权重（反比例关系）
+    raw_weights = {task_id: 1 / (reward + epsilon) for task_id, reward in task_rewards.items()}
+
+    # Step 3: 温度缩放
+    scaled_weights = {task_id: weight ** (1 / temperature) for task_id, weight in raw_weights.items()}
+
+    # Step 4: 归一化权重
+    total_weight = sum(scaled_weights.values())
+    normalized_weights = {task_id: weight / total_weight for task_id, weight in scaled_weights.items()}
+
+    # Step 5: 裁剪权重，确保在 [min_weight, max_weight] 范围内
+    clipped_weights = {task_id: np.clip(weight, min_weight, max_weight) for task_id, weight in normalized_weights.items()}
+
+    final_weights = clipped_weights
+    return final_weights
+
+task_rewards_list = [
+    {"task1": 10, "task2": 100, "task3": 1000, "task4": 500, "task5": 300},
+    {"task1": 1, "task2": 10, "task3": 100, "task4": 1000, "task5": 10000},
+    {"task1": 0.1, "task2": 0.5, "task3": 0.9, "task4": 5, "task5": 10},
+]
+
+for i, task_rewards in enumerate(task_rewards_list, start=1):
+    print(f"Case {i}: Original Rewards: {task_rewards}")
+    print("Original Weights:")
+    print(compute_task_weights(task_rewards, use_symlog=False))
+    print("Improved Weights with Symlog:")
+    print(compute_task_weights(task_rewards, use_symlog=True))
+    print()