initial movel changes to fix performance
This commit is contained in:
Dobromir Popov
@ -8,6 +8,7 @@ from typing import Tuple, List
|
||||
import os
|
||||
import sys
|
||||
import logging
|
||||
import torch.nn.functional as F
|
||||
|
||||
# Add parent directory to path
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
|
||||
@ -20,71 +21,124 @@ logger = logging.getLogger(__name__)
|
||||
class DQNAgent:
|
||||
"""
|
||||
Deep Q-Network agent for trading
|
||||
Uses CNN model as the base network
|
||||
Uses CNN model as the base network with GPU support
|
||||
"""
|
||||
def __init__(self,
|
||||
state_size: int,
|
||||
action_size: int,
|
||||
window_size: int,
|
||||
num_features: int,
|
||||
timeframes: List[str],
|
||||
state_shape: Tuple[int, ...],
|
||||
n_actions: int,
|
||||
learning_rate: float = 0.0005, # Reduced learning rate for more stability
|
||||
gamma: float = 0.97, # Slightly reduced discount factor
|
||||
epsilon: float = 1.0,
|
||||
epsilon_min: float = 0.05, # Increased minimum epsilon for more exploration
|
||||
epsilon_decay: float = 0.9975, # Slower decay rate
|
||||
memory_size: int = 20000, # Increased memory size
|
||||
buffer_size: int = 20000, # Increased memory size
|
||||
batch_size: int = 128, # Larger batch size
|
||||
target_update: int = 5): # More frequent target updates
|
||||
target_update: int = 5, # More frequent target updates
|
||||
device=None): # Device for computations
|
||||
|
||||
self.state_size = state_size
|
||||
self.action_size = action_size
|
||||
self.window_size = window_size
|
||||
self.num_features = num_features
|
||||
self.timeframes = timeframes
|
||||
# Extract state dimensions
|
||||
if isinstance(state_shape, tuple) and len(state_shape) > 1:
|
||||
# Multi-dimensional state (like image or sequence)
|
||||
self.state_dim = state_shape
|
||||
else:
|
||||
# 1D state
|
||||
if isinstance(state_shape, tuple):
|
||||
self.state_dim = state_shape[0]
|
||||
else:
|
||||
self.state_dim = state_shape
|
||||
|
||||
# Store parameters
|
||||
self.n_actions = n_actions
|
||||
self.learning_rate = learning_rate
|
||||
self.gamma = gamma
|
||||
self.epsilon = epsilon
|
||||
self.epsilon_min = epsilon_min
|
||||
self.epsilon_decay = epsilon_decay
|
||||
self.memory_size = memory_size
|
||||
self.buffer_size = buffer_size
|
||||
self.batch_size = batch_size
|
||||
self.target_update = target_update
|
||||
|
||||
# Device configuration
|
||||
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||||
# Set device for computation (default to CPU)
|
||||
if device is None:
|
||||
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||||
else:
|
||||
self.device = device
|
||||
|
||||
# Initialize networks
|
||||
self.policy_net = CNNModelPyTorch(
|
||||
window_size=window_size,
|
||||
num_features=num_features,
|
||||
output_size=action_size,
|
||||
timeframes=timeframes
|
||||
).to(self.device)
|
||||
# Initialize models with appropriate architecture based on state shape
|
||||
if isinstance(self.state_dim, tuple) and len(self.state_dim) > 1:
|
||||
# For image-like states (from RL environment with CNN)
|
||||
from NN.models.simple_cnn import SimpleCNN
|
||||
self.policy_net = SimpleCNN(self.state_dim, self.n_actions)
|
||||
self.target_net = SimpleCNN(self.state_dim, self.n_actions)
|
||||
else:
|
||||
# For 1D state vectors (most environments)
|
||||
from NN.models.simple_mlp import SimpleMLP
|
||||
self.policy_net = SimpleMLP(self.state_dim, self.n_actions)
|
||||
self.target_net = SimpleMLP(self.state_dim, self.n_actions)
|
||||
|
||||
self.target_net = CNNModelPyTorch(
|
||||
window_size=window_size,
|
||||
num_features=num_features,
|
||||
output_size=action_size,
|
||||
timeframes=timeframes
|
||||
).to(self.device)
|
||||
# Initialize the target network with the same weights as the policy network
|
||||
self.target_net.load_state_dict(self.policy_net.state_dict())
|
||||
|
||||
# Initialize optimizer with gradient clipping
|
||||
self.optimizer = optim.Adam(self.policy_net.parameters(), lr=learning_rate, weight_decay=1e-5)
|
||||
# Set models to eval mode (important for batch norm, dropout)
|
||||
self.target_net.eval()
|
||||
|
||||
# Initialize memories with different priorities
|
||||
self.memory = deque(maxlen=memory_size)
|
||||
self.extrema_memory = deque(maxlen=memory_size // 4) # For extrema points
|
||||
self.positive_memory = deque(maxlen=memory_size // 4) # For positive rewards
|
||||
# Optimization components
|
||||
self.optimizer = optim.Adam(self.policy_net.parameters(), lr=self.learning_rate)
|
||||
self.criterion = nn.MSELoss()
|
||||
|
||||
# Training metrics
|
||||
# Experience replay memory
|
||||
self.memory = []
|
||||
self.positive_memory = [] # Special memory for storing good experiences
|
||||
self.update_count = 0
|
||||
self.losses = []
|
||||
self.avg_reward = 0
|
||||
self.no_improvement_count = 0
|
||||
self.best_reward = float('-inf')
|
||||
|
||||
# Extrema detection tracking
|
||||
self.last_extrema_pred = {
|
||||
'class': 2, # Default to "neither" (not extrema)
|
||||
'confidence': 0.0,
|
||||
'raw': None
|
||||
}
|
||||
self.extrema_memory = [] # Special memory for storing extrema points
|
||||
|
||||
# Performance tracking
|
||||
self.losses = []
|
||||
self.avg_reward = 0.0
|
||||
self.best_reward = -float('inf')
|
||||
self.no_improvement_count = 0
|
||||
|
||||
# Check if mixed precision training should be used
|
||||
self.use_mixed_precision = False
|
||||
if torch.cuda.is_available() and hasattr(torch.cuda, 'amp') and 'DISABLE_MIXED_PRECISION' not in os.environ:
|
||||
self.use_mixed_precision = True
|
||||
self.scaler = torch.cuda.amp.GradScaler()
|
||||
logger.info("Mixed precision training enabled")
|
||||
else:
|
||||
logger.info("Mixed precision training disabled")
|
||||
|
||||
# Track if we're in training mode
|
||||
self.training = True
|
||||
|
||||
# For compatibility with old code
|
||||
self.state_size = np.prod(state_shape)
|
||||
self.action_size = n_actions
|
||||
self.memory_size = buffer_size
|
||||
self.timeframes = ["1m", "5m", "15m"][:self.state_dim[0]] # Default timeframes
|
||||
|
||||
logger.info(f"DQN Agent using device: {self.device}")
|
||||
|
||||
def move_models_to_device(self, device=None):
|
||||
"""Move models to the specified device (GPU/CPU)"""
|
||||
if device is not None:
|
||||
self.device = device
|
||||
|
||||
try:
|
||||
self.policy_net = self.policy_net.to(self.device)
|
||||
self.target_net = self.target_net.to(self.device)
|
||||
logger.info(f"Moved models to {self.device}")
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to move models to {self.device}: {str(e)}")
|
||||
return False
|
||||
|
||||
def remember(self, state: np.ndarray, action: int, reward: float,
|
||||
next_state: np.ndarray, done: bool, is_extrema: bool = False):
|
||||
"""
|
||||
@ -103,25 +157,472 @@ class DQNAgent:
|
||||
# Always add to main memory
|
||||
self.memory.append(experience)
|
||||
|
||||
# Add to specialized memories if applicable
|
||||
if is_extrema:
|
||||
# Check if this is an extrema point based on our extrema detection head
|
||||
if hasattr(self, 'last_extrema_pred') and self.last_extrema_pred['class'] != 2:
|
||||
# Class 0 = bottom, 1 = top, 2 = neither
|
||||
# Only consider high confidence predictions
|
||||
if self.last_extrema_pred['confidence'] > 0.7:
|
||||
self.extrema_memory.append(experience)
|
||||
|
||||
# Log this special experience
|
||||
extrema_type = "BOTTOM" if self.last_extrema_pred['class'] == 0 else "TOP"
|
||||
logger.info(f"Stored {extrema_type} experience with reward {reward:.4f}")
|
||||
|
||||
# For tops and bottoms, also duplicate the experience in memory to learn more from it
|
||||
for _ in range(2): # Add 2 extra copies
|
||||
self.memory.append(experience)
|
||||
|
||||
# Explicitly marked extrema points also go to extrema memory
|
||||
elif is_extrema:
|
||||
self.extrema_memory.append(experience)
|
||||
|
||||
# Store positive experiences separately for prioritized replay
|
||||
if reward > 0:
|
||||
self.positive_memory.append(experience)
|
||||
|
||||
# For very good rewards, duplicate to learn more from them
|
||||
if reward > 0.1:
|
||||
for _ in range(min(int(reward * 10), 5)): # Cap at 5 extra copies for very high rewards
|
||||
self.positive_memory.append(experience)
|
||||
|
||||
# Keep memory size under control
|
||||
if len(self.memory) > self.buffer_size:
|
||||
# Keep more recent experiences
|
||||
self.memory = self.memory[-self.buffer_size:]
|
||||
|
||||
# Keep specialized memories under control too
|
||||
if len(self.positive_memory) > self.buffer_size // 4:
|
||||
self.positive_memory = self.positive_memory[-(self.buffer_size // 4):]
|
||||
|
||||
if len(self.extrema_memory) > self.buffer_size // 4:
|
||||
self.extrema_memory = self.extrema_memory[-(self.buffer_size // 4):]
|
||||
|
||||
def act(self, state: np.ndarray, explore=True) -> int:
|
||||
"""Choose action using epsilon-greedy policy with explore flag"""
|
||||
if explore and random.random() < self.epsilon:
|
||||
return random.randrange(self.action_size)
|
||||
return random.randrange(self.n_actions)
|
||||
|
||||
with torch.no_grad():
|
||||
# Ensure state is normalized before inference
|
||||
state_tensor = self._normalize_state(state)
|
||||
state_tensor = torch.FloatTensor(state_tensor).unsqueeze(0).to(self.device)
|
||||
|
||||
# Get predictions using the policy network
|
||||
self.policy_net.eval() # Set to evaluation mode for inference
|
||||
action_probs, extrema_pred = self.policy_net(state_tensor)
|
||||
return action_probs.argmax().item()
|
||||
self.policy_net.train() # Back to training mode
|
||||
|
||||
# Get the predicted extrema class (0=bottom, 1=top, 2=neither)
|
||||
extrema_class = extrema_pred.argmax(dim=1).item()
|
||||
extrema_confidence = torch.softmax(extrema_pred, dim=1)[0, extrema_class].item()
|
||||
|
||||
# Log extrema prediction for significant signals
|
||||
if extrema_confidence > 0.7 and extrema_class != 2: # Only log strong top/bottom signals
|
||||
extrema_type = "BOTTOM" if extrema_class == 0 else "TOP" if extrema_class == 1 else "NEITHER"
|
||||
logger.info(f"High confidence {extrema_type} detected! Confidence: {extrema_confidence:.4f}")
|
||||
|
||||
# Store extrema prediction for the environment to use
|
||||
self.last_extrema_pred = {
|
||||
'class': extrema_class,
|
||||
'confidence': extrema_confidence,
|
||||
'raw': extrema_pred.cpu().numpy()
|
||||
}
|
||||
|
||||
# Get the action with highest Q-value
|
||||
action = action_probs.argmax().item()
|
||||
|
||||
# Adjust action based on extrema prediction (with some probability)
|
||||
if extrema_confidence > 0.8: # Only adjust for strong signals
|
||||
if extrema_class == 0: # Bottom detected
|
||||
# Bias toward BUY at bottoms
|
||||
if action != 0 and random.random() < 0.3 * extrema_confidence:
|
||||
logger.info(f"Adjusting action to BUY based on bottom detection")
|
||||
action = 0 # BUY
|
||||
elif extrema_class == 1: # Top detected
|
||||
# Bias toward SELL at tops
|
||||
if action != 1 and random.random() < 0.3 * extrema_confidence:
|
||||
logger.info(f"Adjusting action to SELL based on top detection")
|
||||
action = 1 # SELL
|
||||
|
||||
return action
|
||||
|
||||
def replay(self, use_prioritized=True) -> float:
|
||||
"""Experience replay - learn from stored experiences
|
||||
|
||||
Args:
|
||||
use_prioritized: Whether to use prioritized experience replay
|
||||
|
||||
Returns:
|
||||
float: Training loss
|
||||
"""
|
||||
# Check if we have enough samples
|
||||
if len(self.memory) < self.batch_size:
|
||||
return 0.0
|
||||
|
||||
# Check if mixed precision should be disabled
|
||||
if 'DISABLE_MIXED_PRECISION' in os.environ:
|
||||
self.use_mixed_precision = False
|
||||
|
||||
# Sample from memory with or without prioritization
|
||||
if use_prioritized and len(self.positive_memory) > self.batch_size // 4:
|
||||
# Use prioritized sampling: mix normal samples with positive reward samples
|
||||
positive_batch_size = min(self.batch_size // 4, len(self.positive_memory))
|
||||
regular_batch_size = self.batch_size - positive_batch_size
|
||||
|
||||
# Get positive examples
|
||||
positive_batch = random.sample(self.positive_memory, positive_batch_size)
|
||||
|
||||
# Get regular examples
|
||||
regular_batch = random.sample(self.memory, regular_batch_size)
|
||||
|
||||
# Combine batches
|
||||
minibatch = positive_batch + regular_batch
|
||||
else:
|
||||
# Use regular uniform sampling
|
||||
minibatch = random.sample(self.memory, self.batch_size)
|
||||
|
||||
# Extract batches with proper tensor conversion
|
||||
states = np.vstack([self._normalize_state(x[0]) for x in minibatch])
|
||||
actions = np.array([x[1] for x in minibatch])
|
||||
rewards = np.array([x[2] for x in minibatch])
|
||||
next_states = np.vstack([self._normalize_state(x[3]) for x in minibatch])
|
||||
dones = np.array([x[4] for x in minibatch], dtype=np.float32)
|
||||
|
||||
# Convert to torch tensors and move to device
|
||||
states_tensor = torch.FloatTensor(states).to(self.device)
|
||||
actions_tensor = torch.LongTensor(actions).to(self.device)
|
||||
rewards_tensor = torch.FloatTensor(rewards).to(self.device)
|
||||
next_states_tensor = torch.FloatTensor(next_states).to(self.device)
|
||||
dones_tensor = torch.FloatTensor(dones).to(self.device)
|
||||
|
||||
# First training step with mixed precision if available
|
||||
if self.use_mixed_precision:
|
||||
loss = self._replay_mixed_precision(
|
||||
states_tensor, actions_tensor, rewards_tensor,
|
||||
next_states_tensor, dones_tensor
|
||||
)
|
||||
else:
|
||||
loss = self._replay_standard(
|
||||
states_tensor, actions_tensor, rewards_tensor,
|
||||
next_states_tensor, dones_tensor
|
||||
)
|
||||
|
||||
# Occasionally train specifically on extrema points, if we have enough
|
||||
if hasattr(self, 'extrema_memory') and len(self.extrema_memory) >= self.batch_size // 2:
|
||||
if random.random() < 0.3: # 30% chance to do extra extrema training
|
||||
# Sample from extrema memory
|
||||
extrema_batch_size = min(self.batch_size // 2, len(self.extrema_memory))
|
||||
extrema_batch = random.sample(self.extrema_memory, extrema_batch_size)
|
||||
|
||||
# Extract batches with proper tensor conversion
|
||||
extrema_states = np.vstack([self._normalize_state(x[0]) for x in extrema_batch])
|
||||
extrema_actions = np.array([x[1] for x in extrema_batch])
|
||||
extrema_rewards = np.array([x[2] for x in extrema_batch])
|
||||
extrema_next_states = np.vstack([self._normalize_state(x[3]) for x in extrema_batch])
|
||||
extrema_dones = np.array([x[4] for x in extrema_batch], dtype=np.float32)
|
||||
|
||||
# Convert to torch tensors and move to device
|
||||
extrema_states_tensor = torch.FloatTensor(extrema_states).to(self.device)
|
||||
extrema_actions_tensor = torch.LongTensor(extrema_actions).to(self.device)
|
||||
extrema_rewards_tensor = torch.FloatTensor(extrema_rewards).to(self.device)
|
||||
extrema_next_states_tensor = torch.FloatTensor(extrema_next_states).to(self.device)
|
||||
extrema_dones_tensor = torch.FloatTensor(extrema_dones).to(self.device)
|
||||
|
||||
# Additional training step focused on extrema points (with smaller learning rate)
|
||||
original_lr = self.optimizer.param_groups[0]['lr']
|
||||
# Temporarily reduce learning rate for fine-tuning on extrema
|
||||
for param_group in self.optimizer.param_groups:
|
||||
param_group['lr'] = original_lr * 0.5
|
||||
|
||||
# Train on extrema
|
||||
if self.use_mixed_precision:
|
||||
extrema_loss = self._replay_mixed_precision(
|
||||
extrema_states_tensor, extrema_actions_tensor, extrema_rewards_tensor,
|
||||
extrema_next_states_tensor, extrema_dones_tensor
|
||||
)
|
||||
else:
|
||||
extrema_loss = self._replay_standard(
|
||||
extrema_states_tensor, extrema_actions_tensor, extrema_rewards_tensor,
|
||||
extrema_next_states_tensor, extrema_dones_tensor
|
||||
)
|
||||
|
||||
# Restore original learning rate
|
||||
for param_group in self.optimizer.param_groups:
|
||||
param_group['lr'] = original_lr
|
||||
|
||||
logger.info(f"Extra training on extrema points: loss={extrema_loss:.4f}")
|
||||
|
||||
# Average the loss
|
||||
loss = (loss + extrema_loss) / 2
|
||||
|
||||
# Store and return loss
|
||||
self.losses.append(loss)
|
||||
return loss
|
||||
|
||||
def _replay_standard(self, states, actions, rewards, next_states, dones):
|
||||
"""Standard precision training step"""
|
||||
# Zero gradients
|
||||
self.optimizer.zero_grad()
|
||||
|
||||
# Get current Q values and extrema predictions
|
||||
current_q_values, current_extrema_pred = self.policy_net(states)
|
||||
current_q_values = current_q_values.gather(1, actions.unsqueeze(1)).squeeze(1)
|
||||
|
||||
# Get next Q values from target network
|
||||
with torch.no_grad():
|
||||
next_q_values, next_extrema_pred = self.target_net(next_states)
|
||||
next_q_values = next_q_values.max(1)[0]
|
||||
|
||||
# Check for dimension mismatch and fix it
|
||||
if rewards.shape[0] != next_q_values.shape[0]:
|
||||
# Log the shape mismatch for debugging
|
||||
logger.warning(f"Shape mismatch detected in standard replay: rewards {rewards.shape}, next_q_values {next_q_values.shape}")
|
||||
# Use the smaller size to prevent index errors
|
||||
min_size = min(rewards.shape[0], next_q_values.shape[0])
|
||||
rewards = rewards[:min_size]
|
||||
dones = dones[:min_size]
|
||||
next_q_values = next_q_values[:min_size]
|
||||
current_q_values = current_q_values[:min_size]
|
||||
|
||||
target_q_values = rewards + (1 - dones) * self.gamma * next_q_values
|
||||
|
||||
# Compute Q-value loss (primary task)
|
||||
q_loss = nn.MSELoss()(current_q_values, target_q_values)
|
||||
|
||||
# Create extrema labels from price movements (crude approximation)
|
||||
# If the next state price is higher than current, we might be in an uptrend (not a bottom)
|
||||
# If the next state price is lower than current, we might be in a downtrend (not a top)
|
||||
# This is a simplified approximation; in real scenarios we'd want to use actual extrema detection
|
||||
|
||||
# Try to extract price from current and next states
|
||||
# Assuming price is in the last feature
|
||||
try:
|
||||
# Extract price feature from sequence data (if available)
|
||||
if len(states.shape) == 3: # [batch, seq, features]
|
||||
current_prices = states[:, -1, -1] # Last timestep, last feature
|
||||
next_prices = next_states[:, -1, -1]
|
||||
else: # [batch, features]
|
||||
current_prices = states[:, -1] # Last feature
|
||||
next_prices = next_states[:, -1]
|
||||
|
||||
# Compute price changes
|
||||
price_changes = (next_prices - current_prices) / current_prices
|
||||
|
||||
# Create crude extrema labels:
|
||||
# 0 = bottom: Large negative price change followed by positive change
|
||||
# 1 = top: Large positive price change followed by negative change
|
||||
# 2 = neither: Small or inconsistent changes
|
||||
|
||||
# Classify based on price change magnitude
|
||||
extrema_labels = torch.ones(min_size, dtype=torch.long, device=self.device) * 2 # Default: neither
|
||||
|
||||
# Identify potential bottoms (significant negative change)
|
||||
bottoms = (price_changes < -0.003)
|
||||
extrema_labels[bottoms] = 0
|
||||
|
||||
# Identify potential tops (significant positive change)
|
||||
tops = (price_changes > 0.003)
|
||||
extrema_labels[tops] = 1
|
||||
|
||||
# Calculate extrema prediction loss (auxiliary task)
|
||||
if len(current_extrema_pred.shape) > 1 and current_extrema_pred.shape[0] >= min_size:
|
||||
current_extrema_pred = current_extrema_pred[:min_size]
|
||||
extrema_loss = nn.CrossEntropyLoss()(current_extrema_pred, extrema_labels)
|
||||
|
||||
# Combined loss (primary + auxiliary with lower weight)
|
||||
# Typically auxiliary tasks should have lower weight to not dominate the primary task
|
||||
loss = q_loss + 0.3 * extrema_loss
|
||||
|
||||
# Log separate loss components occasionally
|
||||
if random.random() < 0.01: # Log 1% of the time to avoid flood
|
||||
logger.info(f"Training losses: Q-loss={q_loss.item():.4f}, Extrema-loss={extrema_loss.item():.4f}")
|
||||
else:
|
||||
# Fall back to just Q-value loss if extrema predictions aren't available
|
||||
loss = q_loss
|
||||
except Exception as e:
|
||||
# Fallback if price extraction fails
|
||||
logger.warning(f"Failed to calculate extrema loss: {str(e)}. Using only Q-value loss.")
|
||||
loss = q_loss
|
||||
|
||||
# Backward pass and optimize
|
||||
loss.backward()
|
||||
|
||||
# Gradient clipping to prevent exploding gradients
|
||||
torch.nn.utils.clip_grad_norm_(self.policy_net.parameters(), 1.0)
|
||||
self.optimizer.step()
|
||||
|
||||
# Update target network if needed
|
||||
self.update_count += 1
|
||||
if self.update_count % self.target_update == 0:
|
||||
self.target_net.load_state_dict(self.policy_net.state_dict())
|
||||
|
||||
# Track and decay epsilon
|
||||
self.epsilon = max(self.epsilon_min, self.epsilon * self.epsilon_decay)
|
||||
|
||||
return loss.item()
|
||||
|
||||
def _replay_mixed_precision(self, states, actions, rewards, next_states, dones):
|
||||
"""Mixed precision training step for better GPU performance"""
|
||||
# Check if mixed precision should be explicitly disabled
|
||||
if 'DISABLE_MIXED_PRECISION' in os.environ:
|
||||
logger.info("Mixed precision explicitly disabled by environment variable")
|
||||
return self._replay_standard(states, actions, rewards, next_states, dones)
|
||||
|
||||
try:
|
||||
# Zero gradients
|
||||
self.optimizer.zero_grad()
|
||||
|
||||
# Forward pass with amp autocasting
|
||||
with torch.cuda.amp.autocast():
|
||||
# Get current Q values and extrema predictions
|
||||
current_q_values, current_extrema_pred = self.policy_net(states)
|
||||
current_q_values = current_q_values.gather(1, actions.unsqueeze(1)).squeeze(1)
|
||||
|
||||
# Get next Q values from target network
|
||||
with torch.no_grad():
|
||||
next_q_values, next_extrema_pred = self.target_net(next_states)
|
||||
next_q_values = next_q_values.max(1)[0]
|
||||
|
||||
# Check for dimension mismatch and fix it
|
||||
if rewards.shape[0] != next_q_values.shape[0]:
|
||||
# Log the shape mismatch for debugging
|
||||
logger.warning(f"Shape mismatch detected: rewards {rewards.shape}, next_q_values {next_q_values.shape}")
|
||||
# Use the smaller size to prevent index errors
|
||||
min_size = min(rewards.shape[0], next_q_values.shape[0])
|
||||
rewards = rewards[:min_size]
|
||||
dones = dones[:min_size]
|
||||
next_q_values = next_q_values[:min_size]
|
||||
current_q_values = current_q_values[:min_size]
|
||||
|
||||
target_q_values = rewards + (1 - dones) * self.gamma * next_q_values
|
||||
|
||||
# Compute Q-value loss (primary task)
|
||||
q_loss = nn.MSELoss()(current_q_values, target_q_values)
|
||||
|
||||
# Create extrema labels from price movements (crude approximation)
|
||||
# Try to extract price from current and next states
|
||||
try:
|
||||
# Extract price feature from sequence data (if available)
|
||||
if len(states.shape) == 3: # [batch, seq, features]
|
||||
current_prices = states[:, -1, -1] # Last timestep, last feature
|
||||
next_prices = next_states[:, -1, -1]
|
||||
else: # [batch, features]
|
||||
current_prices = states[:, -1] # Last feature
|
||||
next_prices = next_states[:, -1]
|
||||
|
||||
# Compute price changes
|
||||
price_changes = (next_prices - current_prices) / current_prices
|
||||
|
||||
# Create crude extrema labels:
|
||||
# 0 = bottom: Large negative price change followed by positive change
|
||||
# 1 = top: Large positive price change followed by negative change
|
||||
# 2 = neither: Small or inconsistent changes
|
||||
|
||||
# Classify based on price change magnitude
|
||||
extrema_labels = torch.ones(min_size, dtype=torch.long, device=self.device) * 2 # Default: neither
|
||||
|
||||
# Identify potential bottoms (significant negative change)
|
||||
bottoms = (price_changes < -0.003)
|
||||
extrema_labels[bottoms] = 0
|
||||
|
||||
# Identify potential tops (significant positive change)
|
||||
tops = (price_changes > 0.003)
|
||||
extrema_labels[tops] = 1
|
||||
|
||||
# Calculate extrema prediction loss (auxiliary task)
|
||||
if len(current_extrema_pred.shape) > 1 and current_extrema_pred.shape[0] >= min_size:
|
||||
current_extrema_pred = current_extrema_pred[:min_size]
|
||||
extrema_loss = nn.CrossEntropyLoss()(current_extrema_pred, extrema_labels)
|
||||
|
||||
# Combined loss (primary + auxiliary with lower weight)
|
||||
loss = q_loss + 0.3 * extrema_loss
|
||||
|
||||
# Log separate loss components occasionally
|
||||
if random.random() < 0.01: # Log 1% of the time to avoid flood
|
||||
logger.info(f"Mixed precision training losses: Q-loss={q_loss.item():.4f}, Extrema-loss={extrema_loss.item():.4f}")
|
||||
else:
|
||||
# Fall back to just Q-value loss
|
||||
loss = q_loss
|
||||
except Exception as e:
|
||||
# Fallback if price extraction fails
|
||||
logger.warning(f"Failed to calculate extrema loss: {str(e)}. Using only Q-value loss.")
|
||||
loss = q_loss
|
||||
|
||||
# Backward pass with scaled gradients
|
||||
self.scaler.scale(loss).backward()
|
||||
|
||||
# Gradient clipping on scaled gradients
|
||||
self.scaler.unscale_(self.optimizer)
|
||||
torch.nn.utils.clip_grad_norm_(self.policy_net.parameters(), 1.0)
|
||||
|
||||
# Update with scaler
|
||||
self.scaler.step(self.optimizer)
|
||||
self.scaler.update()
|
||||
|
||||
# Update target network if needed
|
||||
self.update_count += 1
|
||||
if self.update_count % self.target_update == 0:
|
||||
self.target_net.load_state_dict(self.policy_net.state_dict())
|
||||
|
||||
# Track and decay epsilon
|
||||
self.epsilon = max(self.epsilon_min, self.epsilon * self.epsilon_decay)
|
||||
|
||||
return loss.item()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in mixed precision training: {str(e)}")
|
||||
logger.warning("Falling back to standard precision training")
|
||||
# Fall back to standard training
|
||||
return self._replay_standard(states, actions, rewards, next_states, dones)
|
||||
|
||||
def train_on_extrema(self, states, actions, rewards, next_states, dones):
|
||||
"""
|
||||
Special training function specifically for extrema points
|
||||
|
||||
Args:
|
||||
states: Batch of states at extrema points
|
||||
actions: Batch of actions
|
||||
rewards: Batch of rewards
|
||||
next_states: Batch of next states
|
||||
dones: Batch of done flags
|
||||
|
||||
Returns:
|
||||
float: Training loss
|
||||
"""
|
||||
# Convert to numpy arrays if not already
|
||||
if not isinstance(states, np.ndarray):
|
||||
states = np.array(states)
|
||||
if not isinstance(actions, np.ndarray):
|
||||
actions = np.array(actions)
|
||||
if not isinstance(rewards, np.ndarray):
|
||||
rewards = np.array(rewards)
|
||||
if not isinstance(next_states, np.ndarray):
|
||||
next_states = np.array(next_states)
|
||||
if not isinstance(dones, np.ndarray):
|
||||
dones = np.array(dones, dtype=np.float32)
|
||||
|
||||
# Normalize states
|
||||
states = np.vstack([self._normalize_state(s) for s in states])
|
||||
next_states = np.vstack([self._normalize_state(s) for s in next_states])
|
||||
|
||||
# Convert to torch tensors and move to device
|
||||
states_tensor = torch.FloatTensor(states).to(self.device)
|
||||
actions_tensor = torch.LongTensor(actions).to(self.device)
|
||||
rewards_tensor = torch.FloatTensor(rewards).to(self.device)
|
||||
next_states_tensor = torch.FloatTensor(next_states).to(self.device)
|
||||
dones_tensor = torch.FloatTensor(dones).to(self.device)
|
||||
|
||||
# Choose training method based on precision mode
|
||||
if self.use_mixed_precision:
|
||||
return self._replay_mixed_precision(
|
||||
states_tensor, actions_tensor, rewards_tensor,
|
||||
next_states_tensor, dones_tensor
|
||||
)
|
||||
else:
|
||||
return self._replay_standard(
|
||||
states_tensor, actions_tensor, rewards_tensor,
|
||||
next_states_tensor, dones_tensor
|
||||
)
|
||||
|
||||
def _normalize_state(self, state: np.ndarray) -> np.ndarray:
|
||||
"""Normalize the state data to prevent numerical issues"""
|
||||
@ -211,148 +712,6 @@ class DQNAgent:
|
||||
|
||||
return normalized_state
|
||||
|
||||
def replay(self, use_prioritized=True) -> float:
|
||||
"""
|
||||
Train on a batch of experiences with prioritized sampling
|
||||
|
||||
Args:
|
||||
use_prioritized: Whether to use prioritized replay
|
||||
|
||||
Returns:
|
||||
float: Loss value
|
||||
"""
|
||||
if len(self.memory) < self.batch_size:
|
||||
return 0.0
|
||||
|
||||
# Sample batch with prioritization
|
||||
batch = []
|
||||
|
||||
if use_prioritized and len(self.positive_memory) > 0 and len(self.extrema_memory) > 0:
|
||||
# Prioritized sampling from different memory types
|
||||
positive_count = min(self.batch_size // 4, len(self.positive_memory))
|
||||
extrema_count = min(self.batch_size // 4, len(self.extrema_memory))
|
||||
regular_count = self.batch_size - positive_count - extrema_count
|
||||
|
||||
positive_samples = random.sample(list(self.positive_memory), positive_count)
|
||||
extrema_samples = random.sample(list(self.extrema_memory), extrema_count)
|
||||
regular_samples = random.sample(list(self.memory), regular_count)
|
||||
|
||||
batch = positive_samples + extrema_samples + regular_samples
|
||||
else:
|
||||
# Standard sampling
|
||||
batch = random.sample(self.memory, self.batch_size)
|
||||
|
||||
states, actions, rewards, next_states, dones = zip(*batch)
|
||||
|
||||
# Normalize states before training
|
||||
normalized_states = np.array([self._normalize_state(state) for state in states])
|
||||
normalized_next_states = np.array([self._normalize_state(state) for state in next_states])
|
||||
|
||||
# Convert to tensors and move to device
|
||||
states_tensor = torch.FloatTensor(normalized_states).to(self.device)
|
||||
actions_tensor = torch.LongTensor(actions).to(self.device)
|
||||
rewards_tensor = torch.FloatTensor(rewards).to(self.device)
|
||||
next_states_tensor = torch.FloatTensor(normalized_next_states).to(self.device)
|
||||
dones_tensor = torch.FloatTensor(dones).to(self.device)
|
||||
|
||||
# Get current Q values
|
||||
current_q_values, extrema_pred = self.policy_net(states_tensor)
|
||||
current_q_values = current_q_values.gather(1, actions_tensor.unsqueeze(1))
|
||||
|
||||
# Get next Q values from target network (Double DQN approach)
|
||||
with torch.no_grad():
|
||||
# Get actions from policy network
|
||||
next_actions, _ = self.policy_net(next_states_tensor)
|
||||
next_actions = next_actions.max(1)[1].unsqueeze(1)
|
||||
|
||||
# Get Q values from target network for those actions
|
||||
next_q_values, _ = self.target_net(next_states_tensor)
|
||||
next_q_values = next_q_values.gather(1, next_actions).squeeze(1)
|
||||
|
||||
# Compute target Q values
|
||||
target_q_values = rewards_tensor + (1 - dones_tensor) * self.gamma * next_q_values
|
||||
|
||||
# Clamp target values to prevent extreme values
|
||||
target_q_values = torch.clamp(target_q_values, -100, 100)
|
||||
|
||||
# Compute Huber loss (more robust to outliers than MSE)
|
||||
loss = nn.SmoothL1Loss()(current_q_values.squeeze(), target_q_values)
|
||||
|
||||
# Optimize
|
||||
self.optimizer.zero_grad()
|
||||
loss.backward()
|
||||
|
||||
# Apply gradient clipping to prevent exploding gradients
|
||||
nn.utils.clip_grad_norm_(self.policy_net.parameters(), max_norm=1.0)
|
||||
|
||||
self.optimizer.step()
|
||||
|
||||
# Update target network if needed
|
||||
self.update_count += 1
|
||||
if self.update_count % self.target_update == 0:
|
||||
self.target_net.load_state_dict(self.policy_net.state_dict())
|
||||
|
||||
# Decay epsilon
|
||||
self.epsilon = max(self.epsilon_min, self.epsilon * self.epsilon_decay)
|
||||
|
||||
return loss.item()
|
||||
|
||||
def train_on_extrema(self, states, actions, rewards, next_states, dones):
|
||||
"""
|
||||
Special training method focused on extrema patterns
|
||||
|
||||
Args:
|
||||
states: Array of states near extrema points
|
||||
actions: Correct actions to take (buy at bottoms, sell at tops)
|
||||
rewards: Rewards for each action
|
||||
next_states: Next states
|
||||
dones: Done flags
|
||||
"""
|
||||
if len(states) == 0:
|
||||
return 0.0
|
||||
|
||||
# Normalize states
|
||||
normalized_states = np.array([self._normalize_state(state) for state in states])
|
||||
normalized_next_states = np.array([self._normalize_state(state) for state in next_states])
|
||||
|
||||
# Convert to tensors
|
||||
states_tensor = torch.FloatTensor(normalized_states).to(self.device)
|
||||
actions_tensor = torch.LongTensor(actions).to(self.device)
|
||||
rewards_tensor = torch.FloatTensor(rewards).to(self.device)
|
||||
next_states_tensor = torch.FloatTensor(normalized_next_states).to(self.device)
|
||||
dones_tensor = torch.FloatTensor(dones).to(self.device)
|
||||
|
||||
# Forward pass
|
||||
current_q_values, extrema_pred = self.policy_net(states_tensor)
|
||||
current_q_values = current_q_values.gather(1, actions_tensor.unsqueeze(1))
|
||||
|
||||
# Get next Q values (Double DQN approach)
|
||||
with torch.no_grad():
|
||||
next_actions, _ = self.policy_net(next_states_tensor)
|
||||
next_actions = next_actions.max(1)[1].unsqueeze(1)
|
||||
|
||||
next_q_values, _ = self.target_net(next_states_tensor)
|
||||
next_q_values = next_q_values.gather(1, next_actions).squeeze(1)
|
||||
|
||||
target_q_values = rewards_tensor + (1 - dones_tensor) * self.gamma * next_q_values
|
||||
|
||||
# Clamp target values
|
||||
target_q_values = torch.clamp(target_q_values, -100, 100)
|
||||
|
||||
# Use Huber loss for extrema training
|
||||
q_loss = nn.SmoothL1Loss()(current_q_values.squeeze(), target_q_values)
|
||||
|
||||
# Full loss
|
||||
loss = q_loss
|
||||
|
||||
# Optimize
|
||||
self.optimizer.zero_grad()
|
||||
loss.backward()
|
||||
nn.utils.clip_grad_norm_(self.policy_net.parameters(), max_norm=1.0)
|
||||
self.optimizer.step()
|
||||
|
||||
return loss.item()
|
||||
|
||||
def update_learning_metrics(self, episode_reward, best_reward_threshold=0.01):
|
||||
"""Update learning metrics and perform learning rate adjustments if needed"""
|
||||
# Update average reward with exponential moving average
|
||||
|
||||
Reference in New Issue
Block a user