policy.py

import logging
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from typing import Dict, List, Tuple
from abc import ABC, abstractmethod
from policy.config import Config
from policy.models import PolicyNetwork
from policy.utils import (
    calculate_velocity_threshold,
    calculate_flow_theory,
    calculate_safe_set,
    calculate_control_input,
    calculate_barrier_certificate,
)
from policy.exceptions import PolicyError
from policy.data import PolicyDataset

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class PolicyAgent(ABC):
    def __init__(self, config: Config):
        self.config = config
        self.policy_network = PolicyNetwork(config)
        self.optimizer = optim.Adam(self.policy_network.parameters(), lr=config.lr)
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.policy_network.to(self.device)

    @abstractmethod
    def train(self, dataset: PolicyDataset):
        pass

    @abstractmethod
    def evaluate(self, dataset: PolicyDataset):
        pass

    def predict(self, state: np.ndarray) -> np.ndarray:
        state_tensor = torch.tensor(state, dtype=torch.float32, device=self.device)
        output = self.policy_network(state_tensor)
        return output.cpu().numpy()

class PolicyNetwork(nn.Module):
    def __init__(self, config: Config):
        super(PolicyNetwork, self).__init__()
        self.fc1 = nn.Linear(config.state_dim, config.hidden_dim)
        self.fc2 = nn.Linear(config.hidden_dim, config.action_dim)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

class PolicyDataset(Dataset):
    def __init__(self, data: List[Tuple[np.ndarray, np.ndarray]], config: Config):
        self.data = data
        self.config = config

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index: int) -> Tuple[np.ndarray, np.ndarray]:
        state, action = self.data[index]
        return state, action

class PolicyConfig(Config):
    def __init__(self):
        super(PolicyConfig, self).__init__()
        self.state_dim = 4
        self.action_dim = 2
        self.hidden_dim = 64
        self.lr = 0.001
        self.batch_size = 32
        self.num_epochs = 10

class PolicyError(Exception):
    pass

def calculate_velocity_threshold(state: np.ndarray, config: Config) -> np.ndarray:
    # Implement velocity-threshold calculation from the paper
    pass

def calculate_flow_theory(state: np.ndarray, config: Config) -> np.ndarray:
    # Implement flow-theory calculation from the paper
    pass

def calculate_safe_set(state: np.ndarray, config: Config) -> np.ndarray:
    # Implement safe-set calculation from the paper
    pass

def calculate_control_input(state: np.ndarray, config: Config) -> np.ndarray:
    # Implement control-input calculation from the paper
    pass

def calculate_barrier_certificate(state: np.ndarray, config: Config) -> np.ndarray:
    # Implement barrier-certificate calculation from the paper
    pass

if __name__ == "__main__":
    config = PolicyConfig()
    dataset = PolicyDataset([(np.random.rand(4), np.random.rand(2)) for _ in range(100)], config)
    agent = PolicyAgent(config)
    agent.train(dataset)
    agent.evaluate(dataset)
    state = np.random.rand(4)
    action = agent.predict(state)
    print(action)