"""
Adaptive Learning Systems - Phase 5.2
Reinforcement learning frameworks for agent self-improvement
"""

import asyncio
import logging
from typing import Dict, List, Any, Optional, Tuple, Union
from datetime import datetime, timedelta
from enum import Enum
import numpy as np
import json

from ..storage import SessionDep
from ..domain import AIAgentWorkflow, AgentExecution, AgentStatus

logger = logging.getLogger(__name__)


class LearningAlgorithm(str, Enum):
    """Reinforcement learning algorithms"""
    Q_LEARNING = "q_learning"
    DEEP_Q_NETWORK = "deep_q_network"
    ACTOR_CRITIC = "actor_critic"
    PROXIMAL_POLICY_OPTIMIZATION = "ppo"
    REINFORCE = "reinforce"
    SARSA = "sarsa"


class RewardType(str, Enum):
    """Reward signal types"""
    PERFORMANCE = "performance"
    EFFICIENCY = "efficiency"
    ACCURACY = "accuracy"
    USER_FEEDBACK = "user_feedback"
    TASK_COMPLETION = "task_completion"
    RESOURCE_UTILIZATION = "resource_utilization"


class LearningEnvironment:
    """Safe learning environment for agent training"""
    
    def __init__(self, environment_id: str, config: Dict[str, Any]):
        self.environment_id = environment_id
        self.config = config
        self.state_space = config.get("state_space", {})
        self.action_space = config.get("action_space", {})
        self.safety_constraints = config.get("safety_constraints", {})
        self.max_episodes = config.get("max_episodes", 1000)
        self.max_steps_per_episode = config.get("max_steps_per_episode", 100)
        
    def validate_state(self, state: Dict[str, Any]) -> bool:
        """Validate state against safety constraints"""
        for constraint_name, constraint_config in self.safety_constraints.items():
            if constraint_name == "state_bounds":
                for param, bounds in constraint_config.items():
                    if param in state:
                        value = state[param]
                        if isinstance(bounds, (list, tuple)) and len(bounds) == 2:
                            if not (bounds[0] <= value <= bounds[1]):
                                return False
        return True
    
    def validate_action(self, action: Dict[str, Any]) -> bool:
        """Validate action against safety constraints"""
        for constraint_name, constraint_config in self.safety_constraints.items():
            if constraint_name == "action_bounds":
                for param, bounds in constraint_config.items():
                    if param in action:
                        value = action[param]
                        if isinstance(bounds, (list, tuple)) and len(bounds) == 2:
                            if not (bounds[0] <= value <= bounds[1]):
                                return False
        return True


class ReinforcementLearningAgent:
    """Reinforcement learning agent for adaptive behavior"""
    
    def __init__(self, agent_id: str, algorithm: LearningAlgorithm, config: Dict[str, Any]):
        self.agent_id = agent_id
        self.algorithm = algorithm
        self.config = config
        self.learning_rate = config.get("learning_rate", 0.001)
        self.discount_factor = config.get("discount_factor", 0.95)
        self.exploration_rate = config.get("exploration_rate", 0.1)
        self.exploration_decay = config.get("exploration_decay", 0.995)
        
        # Initialize algorithm-specific components
        if algorithm == LearningAlgorithm.Q_LEARNING:
            self.q_table = {}
        elif algorithm == LearningAlgorithm.DEEP_Q_NETWORK:
            self.neural_network = self._initialize_neural_network()
            self.target_network = self._initialize_neural_network()
        elif algorithm == LearningAlgorithm.ACTOR_CRITIC:
            self.actor_network = self._initialize_neural_network()
            self.critic_network = self._initialize_neural_network()
        
        # Training metrics
        self.training_history = []
        self.performance_metrics = {
            "total_episodes": 0,
            "total_steps": 0,
            "average_reward": 0.0,
            "convergence_episode": None,
            "best_performance": 0.0
        }
    
    def _initialize_neural_network(self) -> Dict[str, Any]:
        """Initialize neural network architecture"""
        # Simplified neural network representation
        return {
            "layers": [
                {"type": "dense", "units": 128, "activation": "relu"},
                {"type": "dense", "units": 64, "activation": "relu"},
                {"type": "dense", "units": 32, "activation": "relu"}
            ],
            "optimizer": "adam",
            "loss_function": "mse"
        }
    
    def get_action(self, state: Dict[str, Any], training: bool = True) -> Dict[str, Any]:
        """Get action using current policy"""
        
        if training and np.random.random() < self.exploration_rate:
            # Exploration: random action
            return self._get_random_action()
        else:
            # Exploitation: best action according to policy
            return self._get_best_action(state)
    
    def _get_random_action(self) -> Dict[str, Any]:
        """Get random action for exploration"""
        # Simplified random action generation
        return {
            "action_type": np.random.choice(["process", "optimize", "delegate"]),
            "parameters": {
                "intensity": np.random.uniform(0.1, 1.0),
                "duration": np.random.uniform(1.0, 10.0)
            }
        }
    
    def _get_best_action(self, state: Dict[str, Any]) -> Dict[str, Any]:
        """Get best action according to current policy"""
        
        if self.algorithm == LearningAlgorithm.Q_LEARNING:
            return self._q_learning_action(state)
        elif self.algorithm == LearningAlgorithm.DEEP_Q_NETWORK:
            return self._dqn_action(state)
        elif self.algorithm == LearningAlgorithm.ACTOR_CRITIC:
            return self._actor_critic_action(state)
        else:
            return self._get_random_action()
    
    def _q_learning_action(self, state: Dict[str, Any]) -> Dict[str, Any]:
        """Q-learning action selection"""
        state_key = self._state_to_key(state)
        
        if state_key not in self.q_table:
            # Initialize Q-values for this state
            self.q_table[state_key] = {
                "process": 0.0,
                "optimize": 0.0,
                "delegate": 0.0
            }
        
        # Select action with highest Q-value
        q_values = self.q_table[state_key]
        best_action = max(q_values, key=q_values.get)
        
        return {
            "action_type": best_action,
            "parameters": {
                "intensity": 0.8,
                "duration": 5.0
            }
        }
    
    def _dqn_action(self, state: Dict[str, Any]) -> Dict[str, Any]:
        """Deep Q-Network action selection"""
        # Simulate neural network forward pass
        state_features = self._extract_state_features(state)
        
        # Simulate Q-value prediction
        q_values = self._simulate_network_forward_pass(state_features)
        
        best_action_idx = np.argmax(q_values)
        actions = ["process", "optimize", "delegate"]
        best_action = actions[best_action_idx]
        
        return {
            "action_type": best_action,
            "parameters": {
                "intensity": 0.7,
                "duration": 6.0
            }
        }
    
    def _actor_critic_action(self, state: Dict[str, Any]) -> Dict[str, Any]:
        """Actor-Critic action selection"""
        # Simulate actor network forward pass
        state_features = self._extract_state_features(state)
        
        # Get action probabilities from actor
        action_probs = self._simulate_actor_forward_pass(state_features)
        
        # Sample action according to probabilities
        action_idx = np.random.choice(len(action_probs), p=action_probs)
        actions = ["process", "optimize", "delegate"]
        selected_action = actions[action_idx]
        
        return {
            "action_type": selected_action,
            "parameters": {
                "intensity": 0.6,
                "duration": 4.0
            }
        }
    
    def _state_to_key(self, state: Dict[str, Any]) -> str:
        """Convert state to hashable key"""
        # Simplified state representation
        key_parts = []
        for key, value in sorted(state.items()):
            if isinstance(value, (int, float)):
                key_parts.append(f"{key}:{value:.2f}")
            elif isinstance(value, str):
                key_parts.append(f"{key}:{value[:10]}")
        
        return "|".join(key_parts)
    
    def _extract_state_features(self, state: Dict[str, Any]) -> List[float]:
        """Extract features from state for neural network"""
        # Simplified feature extraction
        features = []
        
        # Add numerical features
        for key, value in state.items():
            if isinstance(value, (int, float)):
                features.append(float(value))
            elif isinstance(value, str):
                # Simple text encoding
                features.append(float(len(value) % 100))
            elif isinstance(value, bool):
                features.append(float(value))
        
        # Pad or truncate to fixed size
        target_size = 32
        if len(features) < target_size:
            features.extend([0.0] * (target_size - len(features)))
        else:
            features = features[:target_size]
        
        return features
    
    def _simulate_network_forward_pass(self, features: List[float]) -> List[float]:
        """Simulate neural network forward pass"""
        # Simplified neural network computation
        layer_output = features
        
        for layer in self.neural_network["layers"]:
            if layer["type"] == "dense":
                # Simulate dense layer computation
                weights = np.random.randn(len(layer_output), layer["units"])
                layer_output = np.dot(layer_output, weights)
                
                # Apply activation
                if layer["activation"] == "relu":
                    layer_output = np.maximum(0, layer_output)
        
        # Output layer for Q-values
        output_weights = np.random.randn(len(layer_output), 3)  # 3 actions
        q_values = np.dot(layer_output, output_weights)
        
        return q_values.tolist()
    
    def _simulate_actor_forward_pass(self, features: List[float]) -> List[float]:
        """Simulate actor network forward pass"""
        # Similar to DQN but with softmax output
        layer_output = features
        
        for layer in self.neural_network["layers"]:
            if layer["type"] == "dense":
                weights = np.random.randn(len(layer_output), layer["units"])
                layer_output = np.dot(layer_output, weights)
                layer_output = np.maximum(0, layer_output)
        
        # Output layer for action probabilities
        output_weights = np.random.randn(len(layer_output), 3)
        logits = np.dot(layer_output, output_weights)
        
        # Apply softmax
        exp_logits = np.exp(logits - np.max(logits))
        action_probs = exp_logits / np.sum(exp_logits)
        
        return action_probs.tolist()
    
    def update_policy(self, state: Dict[str, Any], action: Dict[str, Any], 
                      reward: float, next_state: Dict[str, Any], done: bool) -> None:
        """Update policy based on experience"""
        
        if self.algorithm == LearningAlgorithm.Q_LEARNING:
            self._update_q_learning(state, action, reward, next_state, done)
        elif self.algorithm == LearningAlgorithm.DEEP_Q_NETWORK:
            self._update_dqn(state, action, reward, next_state, done)
        elif self.algorithm == LearningAlgorithm.ACTOR_CRITIC:
            self._update_actor_critic(state, action, reward, next_state, done)
        
        # Update exploration rate
        self.exploration_rate *= self.exploration_decay
        self.exploration_rate = max(0.01, self.exploration_rate)
    
    def _update_q_learning(self, state: Dict[str, Any], action: Dict[str, Any],
                           reward: float, next_state: Dict[str, Any], done: bool) -> None:
        """Update Q-learning table"""
        state_key = self._state_to_key(state)
        next_state_key = self._state_to_key(next_state)
        
        # Initialize Q-values if needed
        if state_key not in self.q_table:
            self.q_table[state_key] = {"process": 0.0, "optimize": 0.0, "delegate": 0.0}
        if next_state_key not in self.q_table:
            self.q_table[next_state_key] = {"process": 0.0, "optimize": 0.0, "delegate": 0.0}
        
        # Q-learning update rule
        action_type = action["action_type"]
        current_q = self.q_table[state_key][action_type]
        
        if done:
            max_next_q = 0.0
        else:
            max_next_q = max(self.q_table[next_state_key].values())
        
        new_q = current_q + self.learning_rate * (reward + self.discount_factor * max_next_q - current_q)
        self.q_table[state_key][action_type] = new_q
    
    def _update_dqn(self, state: Dict[str, Any], action: Dict[str, Any],
                    reward: float, next_state: Dict[str, Any], done: bool) -> None:
        """Update Deep Q-Network"""
        # Simplified DQN update
        # In real implementation, this would involve gradient descent
        
        # Store experience in replay buffer (simplified)
        experience = {
            "state": state,
            "action": action,
            "reward": reward,
            "next_state": next_state,
            "done": done
        }
        
        # Simulate network update
        self._simulate_network_update(experience)
    
    def _update_actor_critic(self, state: Dict[str, Any], action: Dict[str, Any],
                            reward: float, next_state: Dict[str, Any], done: bool) -> None:
        """Update Actor-Critic networks"""
        # Simplified Actor-Critic update
        experience = {
            "state": state,
            "action": action,
            "reward": reward,
            "next_state": next_state,
            "done": done
        }
        
        # Simulate actor and critic updates
        self._simulate_actor_update(experience)
        self._simulate_critic_update(experience)
    
    def _simulate_network_update(self, experience: Dict[str, Any]) -> None:
        """Simulate neural network weight update"""
        # In real implementation, this would perform backpropagation
        pass
    
    def _simulate_actor_update(self, experience: Dict[str, Any]) -> None:
        """Simulate actor network update"""
        # In real implementation, this would update actor weights
        pass
    
    def _simulate_critic_update(self, experience: Dict[str, Any]) -> None:
        """Simulate critic network update"""
        # In real implementation, this would update critic weights
        pass


class AdaptiveLearningService:
    """Service for adaptive learning systems"""
    
    def __init__(self, session: SessionDep):
        self.session = session
        self.learning_agents = {}
        self.environments = {}
        self.reward_functions = {}
        self.training_sessions = {}
        
    async def create_learning_environment(
        self,
        environment_id: str,
        config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Create safe learning environment"""
        
        try:
            environment = LearningEnvironment(environment_id, config)
            self.environments[environment_id] = environment
            
            return {
                "environment_id": environment_id,
                "status": "created",
                "state_space_size": len(environment.state_space),
                "action_space_size": len(environment.action_space),
                "safety_constraints": len(environment.safety_constraints),
                "max_episodes": environment.max_episodes,
                "created_at": datetime.utcnow().isoformat()
            }
            
        except Exception as e:
            logger.error(f"Failed to create learning environment {environment_id}: {e}")
            raise
    
    async def create_learning_agent(
        self,
        agent_id: str,
        algorithm: LearningAlgorithm,
        config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Create reinforcement learning agent"""
        
        try:
            agent = ReinforcementLearningAgent(agent_id, algorithm, config)
            self.learning_agents[agent_id] = agent
            
            return {
                "agent_id": agent_id,
                "algorithm": algorithm,
                "learning_rate": agent.learning_rate,
                "discount_factor": agent.discount_factor,
                "exploration_rate": agent.exploration_rate,
                "status": "created",
                "created_at": datetime.utcnow().isoformat()
            }
            
        except Exception as e:
            logger.error(f"Failed to create learning agent {agent_id}: {e}")
            raise
    
    async def train_agent(
        self,
        agent_id: str,
        environment_id: str,
        training_config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Train agent in specified environment"""
        
        if agent_id not in self.learning_agents:
            raise ValueError(f"Agent {agent_id} not found")
        
        if environment_id not in self.environments:
            raise ValueError(f"Environment {environment_id} not found")
        
        agent = self.learning_agents[agent_id]
        environment = self.environments[environment_id]
        
        # Initialize training session
        session_id = f"session_{uuid4().hex[:8]}"
        self.training_sessions[session_id] = {
            "agent_id": agent_id,
            "environment_id": environment_id,
            "start_time": datetime.utcnow(),
            "config": training_config,
            "status": "running"
        }
        
        try:
            # Run training episodes
            training_results = await self._run_training_episodes(
                agent, environment, training_config
            )
            
            # Update session
            self.training_sessions[session_id].update({
                "status": "completed",
                "end_time": datetime.utcnow(),
                "results": training_results
            })
            
            return {
                "session_id": session_id,
                "agent_id": agent_id,
                "environment_id": environment_id,
                "training_results": training_results,
                "status": "completed"
            }
            
        except Exception as e:
            self.training_sessions[session_id]["status"] = "failed"
            self.training_sessions[session_id]["error"] = str(e)
            logger.error(f"Training failed for session {session_id}: {e}")
            raise
    
    async def _run_training_episodes(
        self,
        agent: ReinforcementLearningAgent,
        environment: LearningEnvironment,
        config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Run training episodes"""
        
        max_episodes = config.get("max_episodes", environment.max_episodes)
        max_steps = config.get("max_steps_per_episode", environment.max_steps_per_episode)
        target_performance = config.get("target_performance", 0.8)
        
        episode_rewards = []
        episode_lengths = []
        convergence_episode = None
        
        for episode in range(max_episodes):
            # Reset environment
            state = self._reset_environment(environment)
            episode_reward = 0.0
            steps = 0
            
            # Run episode
            for step in range(max_steps):
                # Get action from agent
                action = agent.get_action(state, training=True)
                
                # Validate action
                if not environment.validate_action(action):
                    # Use safe default action
                    action = {"action_type": "process", "parameters": {"intensity": 0.5}}
                
                # Execute action in environment
                next_state, reward, done = self._execute_action(environment, state, action)
                
                # Validate next state
                if not environment.validate_state(next_state):
                    # Reset to safe state
                    next_state = self._get_safe_state(environment)
                    reward = -1.0  # Penalty for unsafe state
                
                # Update agent policy
                agent.update_policy(state, action, reward, next_state, done)
                
                episode_reward += reward
                steps += 1
                state = next_state
                
                if done:
                    break
            
            episode_rewards.append(episode_reward)
            episode_lengths.append(steps)
            
            # Check for convergence
            if len(episode_rewards) >= 10:
                recent_avg = np.mean(episode_rewards[-10:])
                if recent_avg >= target_performance and convergence_episode is None:
                    convergence_episode = episode
            
            # Early stopping if converged
            if convergence_episode is not None and episode > convergence_episode + 50:
                break
        
        # Update agent performance metrics
        agent.performance_metrics.update({
            "total_episodes": len(episode_rewards),
            "total_steps": sum(episode_lengths),
            "average_reward": np.mean(episode_rewards),
            "convergence_episode": convergence_episode,
            "best_performance": max(episode_rewards) if episode_rewards else 0.0
        })
        
        return {
            "episodes_completed": len(episode_rewards),
            "total_steps": sum(episode_lengths),
            "average_reward": float(np.mean(episode_rewards)),
            "best_episode_reward": float(max(episode_rewards)) if episode_rewards else 0.0,
            "convergence_episode": convergence_episode,
            "final_exploration_rate": agent.exploration_rate,
            "training_efficiency": self._calculate_training_efficiency(episode_rewards, convergence_episode)
        }
    
    def _reset_environment(self, environment: LearningEnvironment) -> Dict[str, Any]:
        """Reset environment to initial state"""
        # Simulate environment reset
        return {
            "position": 0.0,
            "velocity": 0.0,
            "task_progress": 0.0,
            "resource_level": 1.0,
            "error_count": 0
        }
    
    def _execute_action(
        self,
        environment: LearningEnvironment,
        state: Dict[str, Any],
        action: Dict[str, Any]
    ) -> Tuple[Dict[str, Any], float, bool]:
        """Execute action in environment"""
        
        action_type = action["action_type"]
        parameters = action.get("parameters", {})
        intensity = parameters.get("intensity", 0.5)
        
        # Simulate action execution
        next_state = state.copy()
        reward = 0.0
        done = False
        
        if action_type == "process":
            # Processing action
            next_state["task_progress"] += intensity * 0.1
            next_state["resource_level"] -= intensity * 0.05
            reward = intensity * 0.1
            
        elif action_type == "optimize":
            # Optimization action
            next_state["resource_level"] += intensity * 0.1
            next_state["task_progress"] += intensity * 0.05
            reward = intensity * 0.15
            
        elif action_type == "delegate":
            # Delegation action
            next_state["task_progress"] += intensity * 0.2
            next_state["error_count"] += np.random.random() < 0.1
            reward = intensity * 0.08
        
        # Check termination conditions
        if next_state["task_progress"] >= 1.0:
            reward += 1.0  # Bonus for task completion
            done = True
        elif next_state["resource_level"] <= 0.0:
            reward -= 0.5  # Penalty for resource depletion
            done = True
        elif next_state["error_count"] >= 3:
            reward -= 0.3  # Penalty for too many errors
            done = True
        
        return next_state, reward, done
    
    def _get_safe_state(self, environment: LearningEnvironment) -> Dict[str, Any]:
        """Get safe default state"""
        return {
            "position": 0.0,
            "velocity": 0.0,
            "task_progress": 0.0,
            "resource_level": 0.5,
            "error_count": 0
        }
    
    def _calculate_training_efficiency(
        self,
        episode_rewards: List[float],
        convergence_episode: Optional[int]
    ) -> float:
        """Calculate training efficiency metric"""
        
        if not episode_rewards:
            return 0.0
        
        if convergence_episode is None:
            # No convergence, calculate based on improvement
            if len(episode_rewards) < 2:
                return 0.0
            
            initial_performance = np.mean(episode_rewards[:5])
            final_performance = np.mean(episode_rewards[-5:])
            improvement = (final_performance - initial_performance) / (abs(initial_performance) + 0.001)
            
            return min(1.0, max(0.0, improvement))
        else:
            # Convergence achieved
            convergence_ratio = convergence_episode / len(episode_rewards)
            return 1.0 - convergence_ratio
    
    async def get_agent_performance(self, agent_id: str) -> Dict[str, Any]:
        """Get agent performance metrics"""
        
        if agent_id not in self.learning_agents:
            raise ValueError(f"Agent {agent_id} not found")
        
        agent = self.learning_agents[agent_id]
        
        return {
            "agent_id": agent_id,
            "algorithm": agent.algorithm,
            "performance_metrics": agent.performance_metrics,
            "current_exploration_rate": agent.exploration_rate,
            "policy_size": len(agent.q_table) if hasattr(agent, 'q_table') else "neural_network",
            "last_updated": datetime.utcnow().isoformat()
        }
    
    async def evaluate_agent(
        self,
        agent_id: str,
        environment_id: str,
        evaluation_config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Evaluate agent performance without training"""
        
        if agent_id not in self.learning_agents:
            raise ValueError(f"Agent {agent_id} not found")
        
        if environment_id not in self.environments:
            raise ValueError(f"Environment {environment_id} not found")
        
        agent = self.learning_agents[agent_id]
        environment = self.environments[environment_id]
        
        # Evaluation episodes (no learning)
        num_episodes = evaluation_config.get("num_episodes", 100)
        max_steps = evaluation_config.get("max_steps", environment.max_steps_per_episode)
        
        evaluation_rewards = []
        evaluation_lengths = []
        
        for episode in range(num_episodes):
            state = self._reset_environment(environment)
            episode_reward = 0.0
            steps = 0
            
            for step in range(max_steps):
                # Get action without exploration
                action = agent.get_action(state, training=False)
                next_state, reward, done = self._execute_action(environment, state, action)
                
                episode_reward += reward
                steps += 1
                state = next_state
                
                if done:
                    break
            
            evaluation_rewards.append(episode_reward)
            evaluation_lengths.append(steps)
        
        return {
            "agent_id": agent_id,
            "environment_id": environment_id,
            "evaluation_episodes": num_episodes,
            "average_reward": float(np.mean(evaluation_rewards)),
            "reward_std": float(np.std(evaluation_rewards)),
            "max_reward": float(max(evaluation_rewards)),
            "min_reward": float(min(evaluation_rewards)),
            "average_episode_length": float(np.mean(evaluation_lengths)),
            "success_rate": sum(1 for r in evaluation_rewards if r > 0) / len(evaluation_rewards),
            "evaluation_timestamp": datetime.utcnow().isoformat()
        }
    
    async def create_reward_function(
        self,
        reward_id: str,
        reward_type: RewardType,
        config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Create custom reward function"""
        
        reward_function = {
            "reward_id": reward_id,
            "reward_type": reward_type,
            "config": config,
            "parameters": config.get("parameters", {}),
            "weights": config.get("weights", {}),
            "created_at": datetime.utcnow().isoformat()
        }
        
        self.reward_functions[reward_id] = reward_function
        
        return reward_function
    
    async def calculate_reward(
        self,
        reward_id: str,
        state: Dict[str, Any],
        action: Dict[str, Any],
        next_state: Dict[str, Any],
        context: Dict[str, Any]
    ) -> float:
        """Calculate reward using specified reward function"""
        
        if reward_id not in self.reward_functions:
            raise ValueError(f"Reward function {reward_id} not found")
        
        reward_function = self.reward_functions[reward_id]
        reward_type = reward_function["reward_type"]
        weights = reward_function.get("weights", {})
        
        if reward_type == RewardType.PERFORMANCE:
            return self._calculate_performance_reward(state, action, next_state, weights)
        elif reward_type == RewardType.EFFICIENCY:
            return self._calculate_efficiency_reward(state, action, next_state, weights)
        elif reward_type == RewardType.ACCURACY:
            return self._calculate_accuracy_reward(state, action, next_state, weights)
        elif reward_type == RewardType.USER_FEEDBACK:
            return self._calculate_user_feedback_reward(context, weights)
        elif reward_type == RewardType.TASK_COMPLETION:
            return self._calculate_task_completion_reward(next_state, weights)
        elif reward_type == RewardType.RESOURCE_UTILIZATION:
            return self._calculate_resource_utilization_reward(state, next_state, weights)
        else:
            return 0.0
    
    def _calculate_performance_reward(
        self,
        state: Dict[str, Any],
        action: Dict[str, Any],
        next_state: Dict[str, Any],
        weights: Dict[str, float]
    ) -> float:
        """Calculate performance-based reward"""
        
        reward = 0.0
        
        # Task progress reward
        progress_weight = weights.get("task_progress", 1.0)
        progress_improvement = next_state.get("task_progress", 0) - state.get("task_progress", 0)
        reward += progress_weight * progress_improvement
        
        # Error penalty
        error_weight = weights.get("error_penalty", -1.0)
        error_increase = next_state.get("error_count", 0) - state.get("error_count", 0)
        reward += error_weight * error_increase
        
        return reward
    
    def _calculate_efficiency_reward(
        self,
        state: Dict[str, Any],
        action: Dict[str, Any],
        next_state: Dict[str, Any],
        weights: Dict[str, float]
    ) -> float:
        """Calculate efficiency-based reward"""
        
        reward = 0.0
        
        # Resource efficiency
        resource_weight = weights.get("resource_efficiency", 1.0)
        resource_usage = state.get("resource_level", 1.0) - next_state.get("resource_level", 1.0)
        reward -= resource_weight * abs(resource_usage)  # Penalize resource waste
        
        # Time efficiency
        time_weight = weights.get("time_efficiency", 0.5)
        action_intensity = action.get("parameters", {}).get("intensity", 0.5)
        reward += time_weight * (1.0 - action_intensity)  # Reward lower intensity
        
        return reward
    
    def _calculate_accuracy_reward(
        self,
        state: Dict[str, Any],
        action: Dict[str, Any],
        next_state: Dict[str, Any],
        weights: Dict[str, float]
    ) -> float:
        """Calculate accuracy-based reward"""
        
        # Simplified accuracy calculation
        accuracy_weight = weights.get("accuracy", 1.0)
        
        # Simulate accuracy based on action appropriateness
        action_type = action["action_type"]
        task_progress = next_state.get("task_progress", 0)
        
        if action_type == "process" and task_progress > 0.1:
            accuracy_score = 0.8
        elif action_type == "optimize" and task_progress > 0.05:
            accuracy_score = 0.9
        elif action_type == "delegate" and task_progress > 0.15:
            accuracy_score = 0.7
        else:
            accuracy_score = 0.3
        
        return accuracy_weight * accuracy_score
    
    def _calculate_user_feedback_reward(
        self,
        context: Dict[str, Any],
        weights: Dict[str, float]
    ) -> float:
        """Calculate user feedback-based reward"""
        
        feedback_weight = weights.get("user_feedback", 1.0)
        user_rating = context.get("user_rating", 0.5)  # 0.0 to 1.0
        
        return feedback_weight * user_rating
    
    def _calculate_task_completion_reward(
        self,
        next_state: Dict[str, Any],
        weights: Dict[str, float]
    ) -> float:
        """Calculate task completion reward"""
        
        completion_weight = weights.get("task_completion", 1.0)
        task_progress = next_state.get("task_progress", 0)
        
        if task_progress >= 1.0:
            return completion_weight * 1.0  # Full reward for completion
        else:
            return completion_weight * task_progress  # Partial reward
    
    def _calculate_resource_utilization_reward(
        self,
        state: Dict[str, Any],
        next_state: Dict[str, Any],
        weights: Dict[str, float]
    ) -> float:
        """Calculate resource utilization reward"""
        
        utilization_weight = weights.get("resource_utilization", 1.0)
        
        # Reward optimal resource usage (not too high, not too low)
        resource_level = next_state.get("resource_level", 0.5)
        optimal_level = 0.7
        
        utilization_score = 1.0 - abs(resource_level - optimal_level)
        
        return utilization_weight * utilization_score
