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import numpy as np
from collections import deque
import torch
import torch.nn as nn
import torch.optim as optim
from safetensors.torch import load_file as load_safetensors
from huggingface_hub import hf_hub_download
# -------------------------------
# Neural Network Model
# -------------------------------
class QNetwork(nn.Module):
"""
Deep Q-Network
"""
def __init__(self, state_dim, action_dim):
super(QNetwork, self).__init__()
self.model = nn.Sequential(
nn.Linear(state_dim, 128),
nn.ReLU(),
nn.Linear(128, 128),
nn.ReLU(),
nn.Linear(128, action_dim)
)
def forward(self, x):
return self.model(x)
# -------------------------------
# DQN Agent
# -------------------------------
class RLAgent:
"""
Deep Q-Learning Agent for:
Market Arbitrage + Grid-Aware Control
"""
def __init__(
self,
state_dim,
action_dim=21,
gamma=0.99,
lr=1e-3,
epsilon_start=1.0,
epsilon_min=0.05,
epsilon_decay=0.995,
buffer_size=100000,
batch_size=256,
target_update_freq=500
):
self.state_dim = state_dim
self.action_dim = action_dim
self.gamma = gamma
self.lr = lr
self.epsilon = epsilon_start
self.epsilon_min = epsilon_min
self.epsilon_decay = epsilon_decay
self.batch_size = batch_size
self.target_update_freq = target_update_freq
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Discrete actions mapped to continuous power commands
self.action_values = np.linspace(-1, 1, action_dim)
# Experience Replay Buffer
self.memory = deque(maxlen=buffer_size)
# Networks
self.policy_net = QNetwork(state_dim, action_dim).to(self.device)
self.target_net = QNetwork(state_dim, action_dim).to(self.device)
self.target_net.load_state_dict(self.policy_net.state_dict())
self.target_net.eval()
self.optimizer = optim.Adam(self.policy_net.parameters(), lr=self.lr)
self.loss_fn = nn.MSELoss()
self.learn_step_counter = 0
# ------------------------------------------------
def act(self, state):
"""
Epsilon-greedy action selection.
"""
if np.random.rand() < self.epsilon:
action_idx = random.randrange(self.action_dim)
else:
state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
with torch.no_grad():
q_values = self.policy_net(state)
action_idx = torch.argmax(q_values).item()
return self.action_values[action_idx]
# ------------------------------------------------
def store(self, state, action, reward, next_state, done):
"""
Store experience in replay buffer.
"""
action_idx = np.argmin(np.abs(self.action_values - action))
self.memory.append((state, action_idx, reward, next_state, done))
# ------------------------------------------------
def learn(self):
"""
Sample mini-batch and perform learning step.
"""
if len(self.memory) < self.batch_size:
return
batch = random.sample(self.memory, self.batch_size)
states, actions, rewards, next_states, dones = zip(*batch)
states = torch.FloatTensor(states).to(self.device)
actions = torch.LongTensor(actions).unsqueeze(1).to(self.device)
rewards = torch.FloatTensor(rewards).unsqueeze(1).to(self.device)
next_states = torch.FloatTensor(next_states).to(self.device)
dones = torch.FloatTensor(dones).unsqueeze(1).to(self.device)
# Current Q-values
q_values = self.policy_net(states).gather(1, actions)
# Target Q-values
with torch.no_grad():
max_next_q = self.target_net(next_states).max(1)[0].unsqueeze(1)
q_target = rewards + (1 - dones) * self.gamma * max_next_q
loss = self.loss_fn(q_values, q_target)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# Target network update
self.learn_step_counter += 1
if self.learn_step_counter % self.target_update_freq == 0:
self.target_net.load_state_dict(self.policy_net.state_dict())
# Epsilon decay
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
# ------------------------------------------------
def save(self, path):
torch.save(self.policy_net.state_dict(), path)
# ------------------------------------------------
def load(self, path, use_safetensors=True):
if "/" in path and not os.path.exists(path):
# Probably a Hugging Face Repo ID
print(f"📥 Downloading model from Hugging Face: {path}")
repo_id = path
filename = "dqn_energy_agent.safetensors" if use_safetensors else "dqn_energy_agent.pth"
path = hf_hub_download(repo_id=repo_id, filename=filename)
if path.endswith(".safetensors") or use_safetensors:
state_dict = load_safetensors(path)
else:
state_dict = torch.load(path, map_location=self.device)
self.policy_net.load_state_dict(state_dict)
self.target_net.load_state_dict(self.policy_net.state_dict())
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