789 lines
30 KiB
Python
789 lines
30 KiB
Python
"""
|
||
d3rlpy 训练脚本 - 适配 Unity TrainingDataRecorder 格式
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||
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||
数据格式：
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{
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"State": [802个float],
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"Actions": [{"data": [8个float]}, {"data": [8个float]}, ...], # 合法动作列表（训练时不用，使用AllActions包装）
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"SelectedAction": [8个float], # 专家选择的动作（归一化后）
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"Reward": float,
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"Done": bool # 注意：此字段会被忽略
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}
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Episode 划分策略：
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- 每个 .jsonl 文件 = 一个完整的 Episode（整局游戏）
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- 文件内所有样本的 Done 字段会被忽略
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- 只有文件的最后一个样本会被标记为 Done=True
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- 这符合4X游戏的特性：只有整局游戏结束才是真正的终止状态
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"""
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import os
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import sys
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import json
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import glob
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import numpy as np
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import d3rlpy
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from d3rlpy.dataset import MDPDataset
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from d3rlpy.algos import BC, IQL, CQL
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# ==================== 配置 ====================
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# 训练开关（修改这里选择要训练的算法）
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TRAIN_BC = False # Behavior Cloning（最简单、最快）✅ 推荐用于测试过滤效果
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TRAIN_IQL = False # Implicit Q-Learning（最稳定）
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TRAIN_CQL = True # Conservative Q-Learning（最强大）
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# 路径配置
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# DATA_DIR = r"F:\TrainData\Test" # 数据目录
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DATA_DIR = r"F:\TrainData\TrainingData" # 数据目录
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MODEL_DIR = "./models" # 模型保存目录
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# 训练参数
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STATE_DIM = 802
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ACTION_DIM = 8 # SelectedAction 是 8 个归一化的 float
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BATCH_SIZE = 256
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LEARNING_RATE_BC = 1e-3
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LEARNING_RATE_IQL = 1e-4
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LEARNING_RATE_CQL = 3e-5 # 🔧 第5次调整：再次降低（5e-5在Epoch 28后仍发散）
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LEARNING_RATE_CQL_CRITIC = 2e-5 # 🔧 Critic 也降低，保持与Actor的比例
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LEARNING_RATE_CQL_ALPHA = 5e-6 # 🔧 Alpha 极慢衰减（原1e-5仍过快）
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N_EPOCHS = 25 # ✅ 100 Epochs分析结果：Epoch 20是最佳点，25轮足够
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# 早停配置
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ENABLE_EARLY_STOPPING = True # 是否启用早停
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EARLY_STOP_PATIENCE = 3 # 容忍多少个epoch的性能下降
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EARLY_STOP_DELTA = 20.0 # Actor Loss增长超过此值触发计数
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EARLY_STOP_TEMP_THRESHOLD = 2.5 # Temperature超过此值立即停止
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EARLY_STOP_ACTOR_POSITIVE = True # Actor Loss转正立即停止
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# 数据集自适应训练步数（可选）
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# 如果数据集很大，可以增加训练步数以充分利用数据
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AUTO_ADJUST_STEPS = False # 是否根据数据集大小自动调整训练步数
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# GPU 设置
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USE_GPU = True # 自动检测 GPU
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# ==================== 数据过滤配置 ====================
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ENABLE_FILTERING = False # 是否启用数据过滤
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MIN_EPISODE_REWARD = 300.0 # Episode 最低总奖励阈值（低于此值的整个 Episode 被丢弃）
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MIN_EPISODE_STEPS = 1 # Episode 最少步数（低于此值的 Episode 被丢弃）
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# ==================== 数据加载 ====================
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def load_training_data():
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"""加载训练数据（带数据过滤）"""
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print("=" * 60)
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print("加载训练数据")
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print("=" * 60)
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print(f"数据目录: {DATA_DIR}")
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if ENABLE_FILTERING:
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print(f"\n✅ 数据过滤已启用:")
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print(f" - 最低 Episode 奖励: {MIN_EPISODE_REWARD}")
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print(f" - 最少 Episode 步数: {MIN_EPISODE_STEPS}")
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print(f" - 策略: 以 Episode 为单位过滤，保留高质量完整轨迹")
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# 查找所有 jsonl 文件
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jsonl_files = glob.glob(os.path.join(DATA_DIR, "*.jsonl"))
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if not jsonl_files:
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raise FileNotFoundError(f"未在 {DATA_DIR} 中找到 .jsonl 文件")
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||
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print(f"\n找到 {len(jsonl_files)} 个数据文件")
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||
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if not ENABLE_FILTERING:
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# 不过滤，直接加载所有数据
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return _load_all_data(jsonl_files)
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# ==================== 单次扫描：边读边过滤 ====================
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print("\n开始扫描：统计 Episode 质量并加载高质量数据...")
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observations = []
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actions = []
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||
rewards = []
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terminals = []
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||
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# 统计信息
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all_episode_rewards = [] # 所有Episode的总奖励
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all_episode_steps = [] # 所有Episode的步数
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total_raw_samples = 0
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filtered_samples = 0
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total_episodes = 0
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||
good_episodes = 0
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||
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for file_idx, file_path in enumerate(jsonl_files):
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current_episode_data = [] # 缓存当前 Episode 的所有样本
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current_episode_reward = 0.0 # 当前 Episode 的累计奖励
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try:
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with open(file_path, 'r', encoding='utf-8') as f:
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for line in f:
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if not line.strip():
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continue
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try:
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data = json.loads(line)
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# 解析数据
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state = np.array(data['State'], dtype=np.float32)
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if len(state) != STATE_DIM:
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continue
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selected_action = np.array(data['SelectedAction'], dtype=np.float32)
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if len(selected_action) != ACTION_DIM:
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continue
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reward = float(data['Reward'])
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# 忽略样本中的Done字段，所有样本都标记为未结束
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||
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# 累计奖励
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current_episode_reward += reward
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||
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# 缓存样本（Done暂时设为False）
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||
current_episode_data.append((state, selected_action, reward, 0.0))
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total_raw_samples += 1
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||
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except (json.JSONDecodeError, KeyError) as e:
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continue
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# 文件读取完毕 = Episode 结束
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if len(current_episode_data) > 0:
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# 将最后一个样本标记为 Done=True
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last_state, last_action, last_reward, _ = current_episode_data[-1]
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current_episode_data[-1] = (last_state, last_action, last_reward, 1.0)
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||
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episode_steps = len(current_episode_data)
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total_episodes += 1
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||
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# 记录统计信息
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all_episode_rewards.append(current_episode_reward)
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all_episode_steps.append(episode_steps)
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||
|
||
# 判断是否满足质量要求
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||
if current_episode_reward >= MIN_EPISODE_REWARD and episode_steps >= MIN_EPISODE_STEPS:
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# 保留这个 Episode 的所有样本
|
||
for s, a, r, d in current_episode_data:
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||
observations.append(s)
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||
actions.append(a)
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||
rewards.append(r)
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||
terminals.append(d)
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||
filtered_samples += 1
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||
good_episodes += 1
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||
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||
except Exception as e:
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print(f" 警告: 文件 {os.path.basename(file_path)} 读取失败: {e}")
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continue
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if (file_idx + 1) % 100 == 0:
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print(f" 已处理 {file_idx + 1}/{len(jsonl_files)} 个文件...")
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# 检查是否有有效数据
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if total_episodes == 0:
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raise ValueError("未找到任何完整的 Episode")
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||
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||
if filtered_samples == 0:
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raise ValueError(f"没有 Episode 满足条件（奖励>={MIN_EPISODE_REWARD} 且 步数>={MIN_EPISODE_STEPS}），请降低阈值")
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# 显示统计信息
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print(f"\nEpisode 质量统计:")
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print(f" 总 Episode 数: {total_episodes}")
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print(f" 符合条件的 Episode: {good_episodes} ({good_episodes/total_episodes*100:.1f}%)")
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||
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print(f"\n 奖励分布:")
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||
print(f" 范围: [{min(all_episode_rewards):.2f}, {max(all_episode_rewards):.2f}]")
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print(f" 平均: {np.mean(all_episode_rewards):.2f}")
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print(f" 中位数: {np.median(all_episode_rewards):.2f}")
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print(f" 过滤阈值: >={MIN_EPISODE_REWARD}")
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print(f"\n 步数分布:")
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print(f" 范围: [{min(all_episode_steps)}, {max(all_episode_steps)}]")
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print(f" 平均: {np.mean(all_episode_steps):.1f}")
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print(f" 中位数: {int(np.median(all_episode_steps))}")
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print(f" 过滤阈值: >={MIN_EPISODE_STEPS}")
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||
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# 转换为 numpy 数组
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observations = np.array(observations, dtype=np.float32)
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actions = np.array(actions, dtype=np.float32)
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rewards = np.array(rewards, dtype=np.float32)
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terminals = np.array(terminals, dtype=np.float32)
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||
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print(f"\n过滤完成:")
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||
print(f" 原始样本数: {total_raw_samples}")
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||
print(f" 保留样本数: {filtered_samples}")
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||
print(f" 过滤比例: {(1 - filtered_samples/total_raw_samples)*100:.1f}%")
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print(f" 状态维度: {observations.shape}")
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print(f" 动作维度: {actions.shape}")
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print(f" 奖励范围: [{rewards.min():.2f}, {rewards.max():.2f}]")
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print(f" Episode数: {int(terminals.sum())}")
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# Reward 分布统计
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print(f"\nReward 统计:")
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print(f" 平均 Reward: {rewards.mean():.4f}")
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print(f" Reward 标准差: {rewards.std():.4f}")
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||
nonzero_ratio = (rewards != 0).sum() / len(rewards) * 100
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||
print(f" 非零 Reward 比例: {nonzero_ratio:.2f}%")
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||
if nonzero_ratio < 100:
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||
nonzero_rewards = rewards[rewards != 0]
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||
print(f" 非零 Reward 平均值: {nonzero_rewards.mean():.2f}")
|
||
|
||
# 检查动作是否已归一化
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||
if actions.max() > 1.1 or actions.min() < -0.1:
|
||
print(f"\n⚠️ 警告: 动作值超出 [0,1] 范围")
|
||
print(f" 最小值: {actions.min()}")
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||
print(f" 最大值: {actions.max()}")
|
||
|
||
return observations, actions, rewards, terminals
|
||
|
||
|
||
def _load_all_data(jsonl_files):
|
||
"""不过滤，加载所有数据（每个文件=1个Episode）"""
|
||
observations = []
|
||
actions = []
|
||
rewards = []
|
||
terminals = []
|
||
|
||
total_samples = 0
|
||
total_episodes = 0
|
||
|
||
for file_idx, file_path in enumerate(jsonl_files):
|
||
file_samples = 0
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||
file_data = [] # 缓存当前文件的所有样本
|
||
|
||
try:
|
||
with open(file_path, 'r', encoding='utf-8') as f:
|
||
for line_num, line in enumerate(f, 1):
|
||
if not line.strip():
|
||
continue
|
||
|
||
try:
|
||
data = json.loads(line)
|
||
|
||
state = np.array(data['State'], dtype=np.float32)
|
||
if len(state) != STATE_DIM:
|
||
continue
|
||
|
||
selected_action = np.array(data['SelectedAction'], dtype=np.float32)
|
||
if len(selected_action) != ACTION_DIM:
|
||
continue
|
||
|
||
reward = float(data['Reward'])
|
||
# 忽略样本中的Done字段，统一在文件末尾设置
|
||
|
||
file_data.append((state, selected_action, reward))
|
||
file_samples += 1
|
||
|
||
except (json.JSONDecodeError, KeyError) as e:
|
||
continue
|
||
|
||
# 文件读取完毕，将所有样本添加到数据集
|
||
if len(file_data) > 0:
|
||
for idx, (state, action, reward) in enumerate(file_data):
|
||
observations.append(state)
|
||
actions.append(action)
|
||
rewards.append(reward)
|
||
# 只有最后一个样本标记为Done=True
|
||
terminals.append(1.0 if idx == len(file_data) - 1 else 0.0)
|
||
|
||
total_episodes += 1
|
||
|
||
except Exception as e:
|
||
print(f"错误: 读取文件 {os.path.basename(file_path)} 失败: {e}")
|
||
continue
|
||
|
||
total_samples += file_samples
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||
print(f" [{file_idx+1}/{len(jsonl_files)}] {os.path.basename(file_path)}: {file_samples} 样本")
|
||
|
||
if total_samples == 0:
|
||
raise ValueError("没有加载到任何有效样本")
|
||
|
||
observations = np.array(observations, dtype=np.float32)
|
||
actions = np.array(actions, dtype=np.float32)
|
||
rewards = np.array(rewards, dtype=np.float32)
|
||
terminals = np.array(terminals, dtype=np.float32)
|
||
|
||
print(f"\n加载完成:")
|
||
print(f" 总样本数: {total_samples}")
|
||
print(f" 总文件数(Episode数): {total_episodes}")
|
||
print(f" 状态维度: {observations.shape}")
|
||
print(f" 动作维度: {actions.shape}")
|
||
print(f" 奖励范围: [{rewards.min():.2f}, {rewards.max():.2f}]")
|
||
print(f" Episode终止点数: {int(terminals.sum())}")
|
||
|
||
# Reward 分布统计
|
||
print(f"\nReward 统计:")
|
||
print(f" 平均 Reward: {rewards.mean():.4f}")
|
||
print(f" Reward 标准差: {rewards.std():.4f}")
|
||
nonzero_ratio = (rewards != 0).sum() / len(rewards) * 100
|
||
print(f" 非零 Reward 比例: {nonzero_ratio:.2f}%")
|
||
if nonzero_ratio < 100:
|
||
nonzero_rewards = rewards[rewards != 0]
|
||
print(f" 非零 Reward 平均值: {nonzero_rewards.mean():.2f}")
|
||
|
||
# Reward 范围检查和警告
|
||
max_abs_reward = max(abs(rewards.min()), abs(rewards.max()))
|
||
print(f"\nReward 范围诊断:")
|
||
print(f" 最大绝对值: {max_abs_reward:.2f}")
|
||
|
||
if max_abs_reward > 100000:
|
||
print(f" ❌ 严重警告: Reward 范围过大（> 100000）")
|
||
print(f" 建议: 在 C# 端缩放 reward *= {1000/max_abs_reward:.6f}")
|
||
print(f" 风险: 训练可能不稳定或失败")
|
||
elif max_abs_reward > 10000:
|
||
print(f" ⚠️ 警告: Reward 范围较大（> 10000）")
|
||
print(f" 建议: 考虑缩放 reward *= {1000/max_abs_reward:.6f}")
|
||
print(f" 说明: CQL 可以处理，但缩放会更稳定")
|
||
elif max_abs_reward > 5000:
|
||
print(f" ℹ️ 提示: Reward 范围中等（> 5000）")
|
||
print(f" 说明: CQL 可以处理，训练过程请关注 loss 曲线")
|
||
else:
|
||
print(f" ✅ Reward 范围合理（<= 5000）")
|
||
print(f" 说明: 可以直接训练，CQL 会自适应")
|
||
|
||
return observations, actions, rewards, terminals
|
||
|
||
|
||
# ==================== 训练函数 ====================
|
||
|
||
def train_bc(dataset):
|
||
"""训练 BC 算法"""
|
||
if not TRAIN_BC:
|
||
return None
|
||
|
||
print("\n" + "=" * 60)
|
||
print("训练 BC (Behavior Cloning)")
|
||
print("=" * 60)
|
||
|
||
# d3rlpy 2.x 使用 BCConfig 配置
|
||
from d3rlpy.algos import BCConfig
|
||
import torch
|
||
|
||
# 设置设备
|
||
device = "cuda:0" if USE_GPU and torch.cuda.is_available() else "cpu"
|
||
print(f"使用设备: {device}")
|
||
if device.startswith("cuda"):
|
||
print(f"GPU: {torch.cuda.get_device_name(0)}")
|
||
print(f"显存: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f} GB")
|
||
|
||
config = BCConfig(
|
||
batch_size=BATCH_SIZE,
|
||
learning_rate=LEARNING_RATE_BC # d3rlpy 2.x 支持此参数
|
||
)
|
||
bc = config.create(device=device)
|
||
|
||
# 自动调整训练步数
|
||
if AUTO_ADJUST_STEPS:
|
||
# 计算一个"真正的Epoch"需要多少步
|
||
dataset_size = len(dataset)
|
||
steps_per_real_epoch = max(1, dataset_size // BATCH_SIZE)
|
||
|
||
# 建议训练步数：至少让每个样本被看到10次
|
||
recommended_steps = steps_per_real_epoch * 10
|
||
actual_steps = max(N_EPOCHS * 1000, recommended_steps)
|
||
|
||
print(f"\n数据集自适应训练:")
|
||
print(f" 数据集大小: {dataset_size} 样本")
|
||
print(f" 1个真实Epoch: {steps_per_real_epoch} 步")
|
||
print(f" 原始训练步数: {N_EPOCHS * 1000}")
|
||
print(f" 建议训练步数: {recommended_steps} (保证每个样本至少被看10次)")
|
||
print(f" 实际训练步数: {actual_steps}")
|
||
|
||
if actual_steps > N_EPOCHS * 1000:
|
||
print(f" ✅ 已自动增加训练步数以充分利用数据")
|
||
else:
|
||
actual_steps = N_EPOCHS * 1000
|
||
|
||
print(f"\n训练参数:")
|
||
print(f" 总步数: {actual_steps}")
|
||
print(f" Batch size: {BATCH_SIZE}")
|
||
print(f" 预计时间: {actual_steps / 1000 * 7 / 60:.1f} 分钟")
|
||
print("\n开始训练...")
|
||
|
||
bc.fit(
|
||
dataset,
|
||
n_steps=actual_steps,
|
||
n_steps_per_epoch=1000,
|
||
show_progress=True
|
||
)
|
||
|
||
# 保存模型
|
||
model_path = os.path.join(MODEL_DIR, "bc_model.d3")
|
||
bc.save_model(model_path)
|
||
print(f"✓ BC 模型已保存: {model_path}")
|
||
print(f" 如需导出 ONNX，请运行: python export_onnx.py")
|
||
|
||
return bc
|
||
|
||
|
||
def train_iql(dataset):
|
||
"""训练 IQL 算法"""
|
||
if not TRAIN_IQL:
|
||
return None
|
||
|
||
print("\n" + "=" * 60)
|
||
print("训练 IQL (Implicit Q-Learning)")
|
||
print("=" * 60)
|
||
|
||
from d3rlpy.algos import IQLConfig
|
||
import torch
|
||
|
||
# 设置设备
|
||
device = "cuda:0" if USE_GPU and torch.cuda.is_available() else "cpu"
|
||
print(f"使用设备: {device}")
|
||
if device.startswith("cuda"):
|
||
print(f"GPU: {torch.cuda.get_device_name(0)}")
|
||
|
||
config = IQLConfig(
|
||
batch_size=BATCH_SIZE,
|
||
actor_learning_rate=LEARNING_RATE_IQL,
|
||
critic_learning_rate=LEARNING_RATE_IQL
|
||
)
|
||
iql = config.create(device=device)
|
||
|
||
print(f"参数: steps={N_EPOCHS * 1000}, batch_size={BATCH_SIZE}")
|
||
print("开始训练...")
|
||
|
||
iql.fit(
|
||
dataset,
|
||
n_steps=N_EPOCHS * 1000,
|
||
n_steps_per_epoch=1000,
|
||
show_progress=True
|
||
)
|
||
|
||
# 保存模型
|
||
model_path = os.path.join(MODEL_DIR, "iql_model.d3")
|
||
iql.save_model(model_path)
|
||
print(f"✓ IQL 模型已保存: {model_path}")
|
||
print(f" 如需导出 ONNX，请运行: python export_onnx.py")
|
||
|
||
return iql
|
||
|
||
|
||
class EarlyStoppingCallback:
|
||
"""早停回调 - 基于100 Epochs训练分析优化"""
|
||
def __init__(self, patience=3, delta=20.0, temp_threshold=2.5, actor_positive_stop=True):
|
||
"""
|
||
Args:
|
||
patience: 容忍多少个epoch的性能下降
|
||
delta: Actor Loss增长超过此值触发计数
|
||
temp_threshold: Temperature超过此值立即停止
|
||
actor_positive_stop: Actor Loss转正是否立即停止
|
||
"""
|
||
self.patience = patience
|
||
self.delta = delta
|
||
self.temp_threshold = temp_threshold
|
||
self.actor_positive_stop = actor_positive_stop
|
||
self.best_actor_loss = float('-inf')
|
||
self.counter = 0
|
||
self.best_epoch = 0
|
||
self.stop_reason = None
|
||
|
||
def check(self, epoch, actor_loss, temp):
|
||
"""
|
||
检查是否应该早停
|
||
|
||
Returns:
|
||
bool: True表示应该停止训练
|
||
"""
|
||
# 规则1: Actor Loss转正，立即停止
|
||
if self.actor_positive_stop and actor_loss > 0:
|
||
self.stop_reason = f"Actor Loss转正 ({actor_loss:.2f})"
|
||
print(f"\n🚨 Early Stop at Epoch {epoch}: {self.stop_reason}")
|
||
print(f" 最佳模型在 Epoch {self.best_epoch} (Actor Loss = {self.best_actor_loss:.2f})")
|
||
return True
|
||
|
||
# 规则2: Temperature超过阈值，立即停止
|
||
if temp > self.temp_threshold:
|
||
self.stop_reason = f"Temperature失控 ({temp:.2f} > {self.temp_threshold})"
|
||
print(f"\n🚨 Early Stop at Epoch {epoch}: {self.stop_reason}")
|
||
print(f" 最佳模型在 Epoch {self.best_epoch} (Actor Loss = {self.best_actor_loss:.2f})")
|
||
return True
|
||
|
||
# 规则3: Actor Loss连续恶化
|
||
if actor_loss > self.best_actor_loss + self.delta:
|
||
self.counter += 1
|
||
print(f"⚠️ Epoch {epoch}: Actor Loss恶化 ({actor_loss:.2f} vs 最佳 {self.best_actor_loss:.2f}), 计数 {self.counter}/{self.patience}")
|
||
|
||
if self.counter >= self.patience:
|
||
self.stop_reason = f"Actor Loss连续 {self.patience} Epochs恶化"
|
||
print(f"\n⚠️ Early Stop at Epoch {epoch}: {self.stop_reason}")
|
||
print(f" 最佳模型在 Epoch {self.best_epoch} (Actor Loss = {self.best_actor_loss:.2f})")
|
||
return True
|
||
else:
|
||
# 更新最佳值
|
||
if actor_loss < self.best_actor_loss:
|
||
self.best_actor_loss = actor_loss
|
||
self.best_epoch = epoch
|
||
print(f"✅ Epoch {epoch}: 新最佳 Actor Loss = {actor_loss:.2f} (Temp = {temp:.2f})")
|
||
self.counter = 0
|
||
|
||
return False
|
||
|
||
|
||
def train_cql(dataset):
|
||
"""训练 CQL 算法"""
|
||
if not TRAIN_CQL:
|
||
return None
|
||
|
||
print("\n" + "=" * 60)
|
||
print("训练 CQL (Conservative Q-Learning)")
|
||
print("=" * 60)
|
||
|
||
from d3rlpy.algos import CQLConfig
|
||
import torch
|
||
|
||
# 设置设备
|
||
device = "cuda:0" if USE_GPU and torch.cuda.is_available() else "cpu"
|
||
print(f"使用设备: {device}")
|
||
if device.startswith("cuda"):
|
||
print(f"GPU: {torch.cuda.get_device_name(0)}")
|
||
|
||
# ✅ 优化后的 CQL 配置
|
||
# 注意：d3rlpy 2.x 不支持直接设置梯度裁剪，但学习率优化仍然有效
|
||
config = CQLConfig(
|
||
batch_size=BATCH_SIZE,
|
||
actor_learning_rate=LEARNING_RATE_CQL, # 3e-4 (提高)
|
||
critic_learning_rate=LEARNING_RATE_CQL_CRITIC, # 1e-4 (适中)
|
||
alpha_learning_rate=LEARNING_RATE_CQL_ALPHA, # 5e-5 (降低)
|
||
temp_learning_rate=1e-4, # 保持默认
|
||
)
|
||
cql = config.create(device=device)
|
||
|
||
print(f"\n🔧 CQL 配置（基于 100 Epochs 完整分析优化）:")
|
||
print(f" Actor Learning Rate: {LEARNING_RATE_CQL} (3e-5，已验证最优)")
|
||
print(f" Critic Learning Rate: {LEARNING_RATE_CQL_CRITIC} (2e-5，保持比例)")
|
||
print(f" Alpha Learning Rate: {LEARNING_RATE_CQL_ALPHA} (5e-6，极慢衰减)")
|
||
print(f" 训练步数: {N_EPOCHS * 1000} ({N_EPOCHS} Epochs)")
|
||
print(f" Batch Size: {BATCH_SIZE}")
|
||
print(f" 早停机制: {'✅ 已启用' if ENABLE_EARLY_STOPPING else '❌ 未启用'}")
|
||
|
||
print("\n📊 100 Epochs 训练关键发现:")
|
||
print(" ✅ Epoch 20: Actor Loss = -124.3 (历史最佳！)")
|
||
print(" ⚠️ Epoch 25-36: Actor Loss 从 -115 → -15 (过拟合)")
|
||
print(" 💥 Epoch 37: Actor Loss = +1.1 (转正爆炸)")
|
||
print(" 💥 Epoch 100: Actor Loss = +1620 (完全失控)")
|
||
|
||
print("\n🎯 优化策略:")
|
||
print(" 1. ✅ 保持学习率 3e-5 (已验证能达到 -124.3)")
|
||
print(" 2. ✅ 训练轮数 100 → 25 (Epoch 20 是黄金点)")
|
||
print(" 3. ✅ 启用早停机制 (监控 Actor Loss 和 Temperature)")
|
||
print(" 4. ✅ Temperature > 2.5 立即停止 (Epoch 37 时 Temp=2.51)")
|
||
print(" 5. ✅ Actor Loss 转正立即停止 (防止爆炸)")
|
||
|
||
print("\n📈 学习率调优完整历史:")
|
||
print(" 尝试1: 3e-4 → Epoch 10爆炸 (Actor +2000)")
|
||
print(" 尝试2: 1e-4 → Epoch 20发散 (Actor +5.7)")
|
||
print(" 尝试3: 1.5e-4 → Epoch 28爆炸 (Actor +228)")
|
||
print(" 尝试4: 5e-5 → Epoch 28发散 (Actor +49)")
|
||
print(" 尝试5: 3e-5 → Epoch 20最优 (Actor -124.3 ⭐)")
|
||
print(" 续 → Epoch 37爆炸 (Actor +1620)")
|
||
|
||
print("\n💡 核心结论:")
|
||
print(" - 学习率 3e-5 是最优配置")
|
||
print(" - 数据集只支持 20-25 Epochs")
|
||
print(" - 必须使用早停机制防止过拟合")
|
||
print(" - 预期本次在 Epoch 20-25 自动停止")
|
||
print("\n开始训练...")
|
||
|
||
# 初始化早停机制
|
||
early_stop = None
|
||
if ENABLE_EARLY_STOPPING:
|
||
early_stop = EarlyStoppingCallback(
|
||
patience=EARLY_STOP_PATIENCE,
|
||
delta=EARLY_STOP_DELTA,
|
||
temp_threshold=EARLY_STOP_TEMP_THRESHOLD,
|
||
actor_positive_stop=EARLY_STOP_ACTOR_POSITIVE
|
||
)
|
||
print(f"\n✅ 早停机制已启用:")
|
||
print(f" - 容忍恶化: {EARLY_STOP_PATIENCE} epochs")
|
||
print(f" - 恶化阈值: Actor Loss 增长 > {EARLY_STOP_DELTA}")
|
||
print(f" - Temperature 警戒线: {EARLY_STOP_TEMP_THRESHOLD}")
|
||
print(f" - Actor Loss 转正立即停止: {EARLY_STOP_ACTOR_POSITIVE}")
|
||
print(f"\n基于 100 Epochs 训练分析:")
|
||
print(f" - Epoch 20 是黄金停止点 (Actor Loss = -124.3)")
|
||
print(f" - Epoch 28+ 开始发散")
|
||
print(f" - 预期本次训练在 Epoch 20-25 自动停止\n")
|
||
|
||
# 手动训练循环以支持早停
|
||
from d3rlpy.logging import FileAdapterFactory
|
||
import datetime
|
||
|
||
# 创建日志目录
|
||
timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
|
||
log_dir = f"d3rlpy_logs/CQL_{timestamp}"
|
||
|
||
best_model_path = None
|
||
stopped_early = False
|
||
|
||
for epoch in range(1, N_EPOCHS + 1):
|
||
# 训练一个epoch
|
||
results = cql.fit(
|
||
dataset,
|
||
n_steps=1000,
|
||
n_steps_per_epoch=1000,
|
||
show_progress=True,
|
||
logger_adapter=FileAdapterFactory(root_dir=log_dir)
|
||
)
|
||
|
||
# 保存checkpoint
|
||
checkpoint_path = os.path.join(MODEL_DIR, f"cql_model_epoch{epoch}.d3")
|
||
cql.save_model(checkpoint_path)
|
||
|
||
# 检查早停
|
||
if ENABLE_EARLY_STOPPING and early_stop:
|
||
# 从结果中提取指标（d3rlpy 2.x 的返回格式）
|
||
actor_loss = results.get('actor_loss', 0.0) if isinstance(results, dict) else 0.0
|
||
temp = results.get('temp', 0.0) if isinstance(results, dict) else 0.0
|
||
|
||
# 注意：d3rlpy 2.x 的 fit 返回值可能不包含详细指标
|
||
# 如果无法获取，我们跳过早停检查
|
||
if actor_loss != 0.0 or temp != 0.0:
|
||
if early_stop.check(epoch, actor_loss, temp):
|
||
stopped_early = True
|
||
best_model_path = os.path.join(MODEL_DIR, f"cql_model_epoch{early_stop.best_epoch}.d3")
|
||
print(f"\n✅ 训练提前停止在 Epoch {epoch}")
|
||
print(f"✅ 最佳模型: Epoch {early_stop.best_epoch}")
|
||
print(f"✅ 停止原因: {early_stop.stop_reason}")
|
||
break
|
||
|
||
# 保存最终模型
|
||
if stopped_early and best_model_path:
|
||
# 复制最佳模型为默认模型
|
||
import shutil
|
||
final_model_path = os.path.join(MODEL_DIR, "cql_model.d3")
|
||
shutil.copy(best_model_path, final_model_path)
|
||
print(f"\n✓ 最佳模型已保存为: {final_model_path}")
|
||
print(f" (来自 Epoch {early_stop.best_epoch})")
|
||
else:
|
||
# 正常结束，保存最后一个epoch的模型
|
||
model_path = os.path.join(MODEL_DIR, "cql_model.d3")
|
||
cql.save_model(model_path)
|
||
print(f"✓ CQL 模型已保存: {model_path}")
|
||
|
||
print(f" 训练日志: {log_dir}")
|
||
print(f" 如需导出 ONNX，请运行: python export_onnx.py")
|
||
|
||
if stopped_early:
|
||
print(f"\n📊 训练总结:")
|
||
print(f" 计划训练: {N_EPOCHS} epochs")
|
||
print(f" 实际训练: {epoch} epochs (提前停止)")
|
||
print(f" 最佳 epoch: {early_stop.best_epoch}")
|
||
print(f" 节省时间: {(N_EPOCHS - epoch) * 26 / 60:.1f} 分钟")
|
||
|
||
return cql
|
||
|
||
|
||
# ==================== 主函数 ====================
|
||
|
||
def main():
|
||
print("""
|
||
╔════════════════════════════════════════════════════════════════╗
|
||
║ d3rlpy 训练脚本 - Unity 数据格式 ║
|
||
╚════════════════════════════════════════════════════════════════╝
|
||
|
||
数据格式：
|
||
State: 802 维 float32（状态）
|
||
SelectedAction: 8 维 float32（归一化后的动作）
|
||
Reward: float32（奖励）
|
||
Done: bool（是否结束）
|
||
|
||
训练配置：
|
||
""")
|
||
|
||
print(f" BC: {'✓ 开启' if TRAIN_BC else '✗ 关闭'}")
|
||
print(f" IQL: {'✓ 开启' if TRAIN_IQL else '✗ 关闭'}")
|
||
print(f" CQL: {'✓ 开启' if TRAIN_CQL else '✗ 关闭'}")
|
||
print()
|
||
|
||
if ENABLE_FILTERING:
|
||
print(f"数据过滤: ✅ 已启用")
|
||
print(f" - Episode 最低奖励: {MIN_EPISODE_REWARD}")
|
||
print(f" - Episode 最少步数: {MIN_EPISODE_STEPS}")
|
||
else:
|
||
print(f"数据过滤: ❌ 未启用（使用全部数据）")
|
||
print()
|
||
|
||
print(f"d3rlpy 版本: {d3rlpy.__version__}")
|
||
|
||
# 检查 GPU
|
||
import torch
|
||
gpu_available = torch.cuda.is_available()
|
||
print(f"GPU 可用: {gpu_available}")
|
||
if gpu_available:
|
||
print(f"GPU 设备: {torch.cuda.get_device_name(0)}")
|
||
print()
|
||
|
||
# 检查数据目录
|
||
if not os.path.exists(DATA_DIR):
|
||
print(f"错误: 数据目录不存在: {DATA_DIR}")
|
||
return
|
||
|
||
# 创建模型目录
|
||
os.makedirs(MODEL_DIR, exist_ok=True)
|
||
|
||
# 加载数据
|
||
try:
|
||
observations, actions, rewards, terminals = load_training_data()
|
||
except Exception as e:
|
||
print(f"\n错误: 数据加载失败")
|
||
print(f"原因: {e}")
|
||
return
|
||
|
||
# 创建数据集
|
||
print("\n" + "=" * 60)
|
||
print("创建 d3rlpy 数据集")
|
||
print("=" * 60)
|
||
|
||
dataset = MDPDataset(
|
||
observations=observations,
|
||
actions=actions,
|
||
rewards=rewards,
|
||
terminals=terminals
|
||
)
|
||
print(f"✓ 数据集创建成功")
|
||
print(f" Action type: Continuous ({ACTION_DIM}D)")
|
||
print(f" Total steps: {len(observations)}")
|
||
|
||
# 训练模型
|
||
if not (TRAIN_BC or TRAIN_IQL or TRAIN_CQL):
|
||
print("\n⚠️ 所有算法都未开启，请修改脚本顶部的开关")
|
||
return
|
||
|
||
trained_models = []
|
||
|
||
if TRAIN_BC:
|
||
bc = train_bc(dataset)
|
||
if bc:
|
||
trained_models.append("BC")
|
||
|
||
if TRAIN_IQL:
|
||
iql = train_iql(dataset)
|
||
if iql:
|
||
trained_models.append("IQL")
|
||
|
||
if TRAIN_CQL:
|
||
cql = train_cql(dataset)
|
||
if cql:
|
||
trained_models.append("CQL")
|
||
|
||
# 完成
|
||
print("\n" + "=" * 60)
|
||
print("训练完成！")
|
||
print("=" * 60)
|
||
print(f"已训练: {', '.join(trained_models)}")
|
||
print(f"模型保存位置: {os.path.abspath(MODEL_DIR)}")
|
||
print()
|
||
print("查看训练日志:")
|
||
print(f" 1. 可视化工具: python view_training_logs.py")
|
||
print(f" 2. 原始日志: d3rlpy_logs/ 目录")
|
||
print()
|
||
print("下一步：")
|
||
print(" 1. 查看训练曲线评估模型质量")
|
||
print(" 2. 导出 ONNX: python export_onnx.py")
|
||
print(" 3. 在 Unity 中使用 ONNX Runtime 加载模型")
|
||
print()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|
||
|