自定义OpenAI Gym元组观测空间RL环境：适配工具及状态空间重构咨询

Great question! Dealing with mixed discrete-continuous observation spaces is a common pain point when moving beyond simple Gym environments, especially since older libraries like keras-rl are no longer maintained. Let’s break down your options into two clear paths: using tools that natively support mixed spaces, or refactoring your observation space to work with more mature RL libraries.

Option 1: Use RL Libraries with Native Mixed Observation Space Support #

Several modern RL toolkits handle Tuple or Dict observation spaces out of the box, or with minimal custom code:

Ray RLlib #

RLlib is one of the best choices here—it’s designed to handle complex, multi-modal observation spaces natively. You won’t need to rewrite your environment’s observation space; just pass your existing CustomEnv to most RLlib trainers (DQN, PPO, SAC, etc.) and it will handle the discrete/continuous split automatically.

Example setup:

import ray
from ray.rllib.agents.dqn import DQNTrainer

# Initialize Ray
ray.init(ignore_reinit_error=True)

# Configure the trainer
config = {
    "env": CustomEnv,  # Your existing environment
    "framework": "torch",  # Or "tf2" if you prefer TensorFlow
    "num_workers": 2,  # Adjust based on your CPU cores
}

# Start training
trainer = DQNTrainer(config=config)
for iteration in range(20):
    results = trainer.train()
    print(f"Iteration {iteration}: Mean Reward = {results['episode_reward_mean']}")

Stable-Baselines3 (with Custom Feature Extractor) #

While Stable-Baselines3 (SB3) doesn’t support Tuple spaces out of the box with default policies, you can easily create a custom feature extractor to handle each component of your observation space separately. This gives you full control over how discrete vs continuous features are processed.

First, define the feature extractor:

from stable_baselines3.common.torch_layers import BaseFeaturesExtractor
import torch.nn as nn
import torch

class MixedSpaceExtractor(BaseFeaturesExtractor):
    def __init__(self, observation_space: spaces.Tuple, features_dim: int = 128):
        super().__init__(observation_space, features_dim)
        
        # Extract each sub-space from the Tuple
        disc1_space, disc2_space, cont1_space, cont2_space = observation_space.spaces
        
        # Process discrete spaces with embeddings
        self.disc1_embedding = nn.Embedding(disc1_space.n, 8)
        self.disc2_embedding = nn.Embedding(disc2_space.n, 2)
        
        # Process continuous spaces with a linear layer
        self.cont_linear = nn.Linear(cont1_space.shape[0] + cont2_space.shape[0], 8)
        
        # Combine all features into a single vector
        self.combined_layer = nn.Linear(8 + 2 + 8, features_dim)

    def forward(self, observations):
        # Split the Tuple observation into components
        disc1, disc2, cont1, cont2 = observations
        
        # Process each component
        disc1_feat = self.disc1_embedding(disc1.long()).flatten(1)
        disc2_feat = self.disc2_embedding(disc2.long()).flatten(1)
        cont_feat = self.cont_linear(torch.cat([cont1, cont2], dim=1))
        
        # Combine and return
        combined = torch.cat([disc1_feat, disc2_feat, cont_feat], dim=1)
        return self.combined_layer(combined)

Then use it with SB3’s DQN:

from stable_baselines3 import DQN

model = DQN(
    "MlpPolicy",
    CustomEnv(),
    policy_kwargs={
        "features_extractor_class": MixedSpaceExtractor,
        "features_extractor_kwargs": {"features_dim": 128}
    },
    verbose=1
)
model.learn(total_timesteps=50000)

CleanRL #

If you prefer a lightweight, no-frills approach, CleanRL provides minimal, modular implementations of RL algorithms. You can modify the observation processing loop directly to handle your Tuple space—no need for complex abstractions. For example, in the DQN implementation, you’d just split the observation tuple and process discrete/continuous parts before feeding them into the Q-network.

Option 2: Refactor Your Observation Space for Compatibility #

If you want to stick with libraries that only support simple Box spaces, you can refactor your observation space into a single continuous vector. Here are two common approaches:

Convert to a Dict Space (More Maintainable) #

Replace your Tuple space with a Dict space, which is easier to work with and supported by many modern libraries. This keeps your observation components semantically separate:

class CustomEnv(gym.Env):
    def __init__(self):
        self.action_space = spaces.Discrete(3)
        self.observation_space = spaces.Dict({
            "discrete_1": spaces.Discrete(16),
            "discrete_2": spaces.Discrete(2),
            "continuous_1": spaces.Box(0, 20000, shape=(1,)),
            "continuous_2": spaces.Box(0, 1000, shape=(1,))
        })
    
    def step(self, action):
        # ... your existing logic ...
        # Return observation as a dict instead of tuple
        return {
            "discrete_1": d1_value,
            "discrete_2": d2_value,
            "continuous_1": np.array([c1_value]),
            "continuous_2": np.array([c2_value])
        }, reward, done, {}

One-Hot Encode Discrete Dimensions into a Single Box Space #

Convert all discrete dimensions to one-hot vectors, then concatenate them with your continuous dimensions to form a single Box space. This works with every RL library that supports continuous observations:

class CustomEnv(gym.Env):
    def __init__(self):
        self.action_space = spaces.Discrete(3)
        # Calculate total dimensions: 16 (one-hot for disc1) + 2 (disc2) +1 +1 =20
        self.observation_space = spaces.Box(
            low=np.concatenate([np.zeros(18), np.array([0, 0])]),
            high=np.concatenate([np.ones(18), np.array([20000, 1000])]),
            dtype=np.float32
        )
    
    def _convert_observation(self, obs_tuple):
        d1, d2, c1, c2 = obs_tuple
        # One-hot encode discrete values
        d1_onehot = np.zeros(16, dtype=np.float32)
        d1_onehot[d1] = 1.0
        d2_onehot = np.zeros(2, dtype=np.float32)
        d2_onehot[d2] = 1.0
        # Concatenate all parts
        return np.concatenate([d1_onehot, d2_onehot, c1, c2])
    
    def step(self, action):
        # ... your existing logic ...
        raw_obs = (d1_value, d2_value, np.array([c1_value]), np.array([c2_value]))
        return self._convert_observation(raw_obs), reward, done, {}

Final Recommendation #

If you want minimal code changes and robust support for mixed spaces, go with Ray RLlib. If you prefer a smaller library and don’t mind writing a custom feature extractor, Stable-Baselines3 is a solid choice. Refactoring to a Box space is the most compatible option but loses semantic separation of your observation components.

内容的提问来源于stack exchange，提问作者Jeff