feature(xjy): add dreamer code for cartpole env

ding/bonus/dreamer/config.py

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+from dataclasses import dataclass
+from typing import Any, Dict, Optional, Sequence
+
+import torch
+
+
+@dataclass
+class DreamerConfig:
+    """Dreamer 全局配置：涵盖模型结构、优化器参数与算法开关。"""
+    # --- 基础环境参数 ---
+    obs_dim: int
+    action_dim: int
+
+    # --- RSSM 核心维度 ---
+    embed_dim: int = 64        # 观测编码后的维度
+    deter_dim: int = 128       # 确定性状态 h_t 的维度 (GRU)
+    stoch_dim: int = 32        # 随机状态 z_t 的维度
+    stoch_classes: int = 32    # (V2/V3) 离散潜在变量的类别数
+    hidden_dim: int = 128      # MLP 隐藏层维度
+
+    # --- 训练超参数 ---
+    model_lr: float = 3e-4     # 世界模型学习率
+    actor_lr: float = 8e-5     # 策略网络学习率
+    critic_lr: float = 8e-5    # 价值网络学习率
+    entropy_scale: float = 1e-3
+    grad_clip: float = 100.0   # 梯度裁剪阈值
+    use_obs_norm: bool = True
+    normalize_advantage: bool = True
+
+    # --- RL 算法参数 ---
+    discount: float = 0.99     # 折扣因子 gamma
+    lambda_: float = 0.95      # Lambda-return 平滑系数
+    horizon: int = 15          # 想象视界长度 H
+
+    # --- Loss 权重 ---
+    free_nats: float = 1.0     # KL 散度的 Free bits 阈值
+    kl_scale: float = 1.0      # KL Loss 权重
+    discount_scale: float = 10.0
+
+    # --- V2 特性开关 ---
+    kl_balance: float = 0.8    # KL Balancing 权重 (0.8 给先验)
+
+    # --- V3 特性开关 ---
+    use_symlog: bool = False   # 是否启用 Symlog 数值压缩
+    target_tau: float = 0.01   # Critic Target 软更新系数
+
+    # --- V3 离散回归配置 ---
+    reward_bins: int = 0       # 奖励离散化的桶数量 (0表示使用标量回归)
+    reward_min: float = -10.0
+    reward_max: float = 10.0
+    value_bins: int = 0        # 价值离散化的桶数量
+    value_min: float = -20.0
+    value_max: float = 20.0
+
+
+@dataclass
+class TrainConfig:
+    """训练流程相关配置。"""
+    env_ids: Sequence[str] = ("CartPole-v1",)
+    agent_versions: Sequence[str] = ("v1",)
+    seeds: Sequence[int] = (42, 2024)
+    total_steps: int = 30_000
+    seed_steps: int = 2_000
+    train_every: int = 1
+    train_steps: int = 1
+    batch_size: int = 32
+    seq_len: int = 8
+    horizon: int = 15
+    replay_size: int = 100_000
+    log_every: int = 1000
+    eval_every: int = 2_000
+    eval_episodes: int = 5
+    workdir: str = "runs/dreamer"
+    workdir_time_format: str = "%Y%m%d_%H%M%S"
+    timestamp_workdir_if_exists: bool = True
+    device: str = "cuda" if torch.cuda.is_available() else "cpu"
+    env_kwargs: Optional[Dict[str, Any]] = None
+
+    # Exploration schedule (epsilon-greedy after seed steps)
+    exploration_epsilon_start: float = 0.10
+    exploration_epsilon_end: float = 0.00
+    exploration_decay_steps: int = 20_000
+
+    # Actor regularization / reward scaling
+    entropy_scale: float = 1e-3
+    model_lr: float = 3e-4
+    actor_lr: float = 3e-4
+    critic_lr: float = 3e-4
+
+    # Model configuration
+    embed_dim: int = 64
+    deter_dim: int = 128
+    stoch_dim: int = 32
+    stoch_classes: int = 32
+    hidden_dim: int = 128
+    use_obs_norm: bool = True
+    normalize_advantage: bool = True
+    discount: float = 0.99
+    lambda_: float = 0.95
+    free_nats: float = 1.0
+    kl_scale: float = 1.0
+    discount_scale: float = 10.0
+    kl_balance: float = 0.8
+    target_tau: float = 0.01
+
+    # 是否每次训练前清空 metrics 文件，避免重复运行时混入历史点
+    overwrite_metrics: bool = True
+
+    # Plotting
+    plot_path: str = "runs/dreamer/return_curve.png"
+
+    def __post_init__(self) -> None:
+        """填充默认环境参数。"""
+        if self.env_kwargs is None:
+            self.env_kwargs = {}

ding/bonus/dreamer/dreamer/__init__.py

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+"""Dreamer components for CartPole."""
+
+from .agent import DreamerV1Agent, DreamerV2Agent, DreamerV3Agent, make_agent
+
+__all__ = ["DreamerV1Agent", "DreamerV2Agent", "DreamerV3Agent", "make_agent"]