"""
Reinforcement Learning Optimizer - Self-improvement through RL algorithms.

This module provides reinforcement learning capabilities for SAFLA optimization:
- Policy Learning: Train and optimize SAFLA's decision-making policies
- Reward Modeling: Define and optimize reward functions based on system goals
- Exploration Strategies: Implement various exploration algorithms for policy discovery
- Training Pipeline: Create end-to-end pipeline for model training and evaluation
- Model Deployment: Seamless integration of trained policies into the system

Key Features:
- Policy Representation: Neural network-based policy models with PyTorch
- Distributed Training: Ray RLlib integration for scalable training
- PPO Algorithm: Implementation of Proximal Policy Optimization
- DQN Algorithm: Deep Q-Network for discrete action spaces
- SAC Algorithm: Soft Actor-Critic for continuous action spaces
- Custom Environments: SAFLA-specific RL environments matching system domains
- Reward Engineering: Configurable reward functions with safety constraints
- Curriculum Learning: Progressive difficulty for efficient training

Technical Requirements:
- PyTorch for neural network models
- Ray RLlib for distributed training
- Gymnasium API compatibility for environments
- Tensorboard integration for experiment tracking
- Model versioning and checkpointing
- A/B testing framework for policy evaluation
- Integration with safety validation framework
"""

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import asyncio
import random
import pickle
import json
from datetime import datetime, timedelta
from typing import List, Dict, Any, Optional, Tuple, Union
from dataclasses import dataclass, field
from enum import Enum
from collections import deque
import logging
import os
from abc import ABC, abstractmethod


class OptimizationStrategy(Enum):
    """Optimization strategy enumeration."""
    AGGRESSIVE = "aggressive"
    CONSERVATIVE = "conservative"
    BALANCED = "balanced"
    ADAPTIVE = "adaptive"


@dataclass
class RLConfig:
    """Configuration for reinforcement learning optimizer."""
    algorithm: str = "dqn"
    state_dim: int = 10
    action_dim: int = 4
    learning_rate: float = 1e-3
    discount_factor: float = 0.99
    epsilon_start: float = 1.0
    epsilon_end: float = 0.01
    epsilon_decay: float = 0.995
    memory_size: int = 10000
    batch_size: int = 32
    target_update_freq: int = 100
    max_episodes: int = 1000
    hidden_dim: int = 128
    num_layers: int = 2
    device: str = "auto"
    
    def __post_init__(self):
        """Validate configuration parameters."""
        if self.state_dim <= 0:
            raise ValueError("State dimension must be positive")
        if self.action_dim <= 0:
            raise ValueError("Action dimension must be positive")
        if self.learning_rate <= 0:
            raise ValueError("Learning rate must be positive")
        if not 0 <= self.discount_factor <= 1:
            raise ValueError("Discount factor must be between 0 and 1")


@dataclass
class AgentState:
    """State representation for RL agent."""
    features: np.ndarray
    timestamp: datetime = field(default_factory=datetime.now)
    metadata: Dict[str, Any] = field(default_factory=dict)


@dataclass
class Action:
    """Action representation for RL agent."""
    value: Union[int, np.ndarray]
    probability: float = 1.0
    metadata: Dict[str, Any] = field(default_factory=dict)


@dataclass
class Reward:
    """Reward representation for RL agent."""
    value: float
    components: Dict[str, float] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class Experience:
    """Experience tuple for replay buffer."""
    state: np.ndarray
    action: Union[int, np.ndarray]
    reward: float
    next_state: np.ndarray
    done: bool
    timestamp: datetime = field(default_factory=datetime.now)
    priority: float = 1.0


@dataclass
class PerformanceMetrics:
    """Performance metrics for RL training."""
    average_reward: float = 0.0
    reward_trend: float = 0.0
    average_steps: float = 0.0
    convergence_rate: float = 0.0
    exploration_rate: float = 0.0
    success_rate: float = 0.0
    episode_count: int = 0
    total_steps: int = 0


@dataclass
class OptimizationStrategy:
    """Strategy for optimization."""
    name: str
    parameters: Dict[str, Any] = field(default_factory=dict)
    performance_score: float = 0.0


class PolicyNetwork(nn.Module):
    """Neural network for policy representation."""
    
    def __init__(self, state_dim: int, action_dim: int, hidden_dim: int = 128, num_layers: int = 2):
        super().__init__()
        self.state_dim = state_dim
        self.action_dim = action_dim
        
        # Build network layers
        layers = []
        input_dim = state_dim
        
        for _ in range(num_layers):
            layers.extend([
                nn.Linear(input_dim, hidden_dim),
                nn.ReLU(),
                nn.Dropout(0.1)
            ])
            input_dim = hidden_dim
        
        layers.append(nn.Linear(hidden_dim, action_dim))
        layers.append(nn.Softmax(dim=-1))
        
        self.network = nn.Sequential(*layers)
    
    def forward(self, state: torch.Tensor) -> torch.Tensor:
        """Forward pass through policy network."""
        return self.network(state)


class QNetwork(nn.Module):
    """Neural network for Q-value estimation."""
    
    def __init__(self, state_dim: int, action_dim: int, hidden_dim: int = 128, num_layers: int = 2):
        super().__init__()
        self.state_dim = state_dim
        self.action_dim = action_dim
        
        # Build network layers
        layers = []
        input_dim = state_dim
        
        for _ in range(num_layers):
            layers.extend([
                nn.Linear(input_dim, hidden_dim),
                nn.ReLU(),
                nn.Dropout(0.1)
            ])
            input_dim = hidden_dim
        
        layers.append(nn.Linear(hidden_dim, action_dim))
        
        self.network = nn.Sequential(*layers)
    
    def forward(self, state: torch.Tensor) -> torch.Tensor:
        """Forward pass through Q-network."""
        return self.network(state)


class ExperienceReplay:
    """Experience replay buffer for RL training."""
    
    def __init__(self, capacity: int = 10000, prioritized: bool = False):
        self.capacity = capacity
        self.prioritized = prioritized
        self.buffer = deque(maxlen=capacity)
        self.priorities = deque(maxlen=capacity) if prioritized else None
        self.position = 0
    
    def store(self, experience: Experience, priority: float = 1.0):
        """Store experience in replay buffer."""
        self.buffer.append(experience)
        if self.prioritized:
            self.priorities.append(priority)
    
    def sample(self, batch_size: int) -> List[Experience]:
        """Sample batch of experiences."""
        if self.size() < batch_size:
            batch_size = self.size()
        
        if self.prioritized and self.priorities:
            # Prioritized sampling
            priorities = np.array(self.priorities)
            probabilities = priorities / np.sum(priorities)
            indices = np.random.choice(len(self.buffer), batch_size, p=probabilities, replace=False)
            return [self.buffer[i] for i in indices]
        else:
            # Uniform sampling
            return random.sample(list(self.buffer), batch_size)
    
    def update_priorities(self, indices: List[int], priorities: List[float]):
        """Update priorities for prioritized experience replay."""
        if self.prioritized and self.priorities:
            for idx, priority in zip(indices, priorities):
                if 0 <= idx < len(self.priorities):
                    self.priorities[idx] = priority
    
    def size(self) -> int:
        """Get current buffer size."""
        return len(self.buffer)
    
    def is_empty(self) -> bool:
        """Check if buffer is empty."""
        return len(self.buffer) == 0
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get buffer statistics."""
        if self.is_empty():
            return {
                "total_experiences": 0,
                "average_reward": 0.0,
                "terminal_state_ratio": 0.0,
                "action_distribution": {}
            }
        
        rewards = [exp.reward for exp in self.buffer]
        terminal_states = sum(1 for exp in self.buffer if exp.done)
        actions = [exp.action for exp in self.buffer if isinstance(exp.action, int)]
        
        action_dist = {}
        for action in actions:
            action_dist[action] = action_dist.get(action, 0) + 1
        
        return {
            "total_experiences": len(self.buffer),
            "average_reward": np.mean(rewards),
            "terminal_state_ratio": terminal_states / len(self.buffer),
            "action_distribution": action_dist
        }


class RewardFunction:
    """Configurable reward function for RL training."""
    
    def __init__(self):
        self.weights = {
            "performance_improvement": 1.0,
            "efficiency_gain": 0.5,
            "resource_usage": -0.1,  # Reduced penalty
            "safety_violation": -2.0
        }
        self.learned_patterns = {}
    
    def calculate_reward(self, context: Dict[str, Any]) -> float:
        """Calculate reward based on context."""
        reward = 0.0
        
        # Basic reward calculation
        for key, weight in self.weights.items():
            if key in context:
                value = context[key]
                # Special handling for resource_usage - penalty only if excessive
                if key == "resource_usage":
                    # Only penalize if resource usage is above threshold (0.9)
                    if value > 0.9:
                        reward += weight * (value - 0.9)
                else:
                    reward += weight * value
        
        # Apply learned patterns
        if "actions" in context:
            pattern_bonus = self._get_pattern_bonus(context["actions"])
            reward += pattern_bonus
        
        return float(reward)
    
    def calculate_multi_objective_reward(self, objectives: Dict[str, float], 
                                       weights: Dict[str, float]) -> float:
        """Calculate multi-objective reward."""
        total_reward = 0.0
        for obj, value in objectives.items():
            if obj in weights:
                total_reward += value * weights[obj]
        return total_reward
    
    def calculate_shaped_reward(self, context: Dict[str, Any]) -> float:
        """Calculate shaped reward for better learning."""
        base_reward = self.calculate_reward(context)
        
        # Add progress-based shaping
        if "progress" in context:
            progress_bonus = context["progress"] * 0.1
            base_reward += progress_bonus
        
        if "goal_distance" in context:
            distance_penalty = context["goal_distance"] * -0.05
            base_reward += distance_penalty
        
        return base_reward
    
    def update_from_episodes(self, episodes: List[Dict[str, Any]]):
        """Update reward function based on episode outcomes."""
        for episode in episodes:
            if "actions" in episode and "outcome" in episode:
                actions_key = str(episode["actions"])
                if episode["outcome"] == "success":
                    self.learned_patterns[actions_key] = self.learned_patterns.get(actions_key, 0) + 0.1
                else:
                    self.learned_patterns[actions_key] = self.learned_patterns.get(actions_key, 0) - 0.1
    
    def _get_pattern_bonus(self, actions: List[int]) -> float:
        """Get bonus based on learned action patterns."""
        actions_key = str(actions)
        return self.learned_patterns.get(actions_key, 0.0)


class QLearningAgent:
    """Q-Learning agent implementation."""
    
    def __init__(self, config: RLConfig):
        self.config = config
        self.epsilon = config.epsilon_start
        self.learning_rate = config.learning_rate
        
        # Initialize Q-table for discrete state space
        # Use reasonable state bins to avoid memory explosion
        self.state_bins = min(5, max(2, 100 // config.state_dim))  # Adaptive binning
        self.state_space_size = self.state_bins ** min(config.state_dim, 6)  # Cap dimensions
        
        # For high-dimensional states, use dictionary-based Q-table
        if config.state_dim > 6 or self.state_space_size > 100000:
            self.q_table = {}  # Dictionary for sparse representation
            self.use_sparse = True
        else:
            self.q_table = np.zeros((self.state_space_size, config.action_dim))
            self.use_sparse = False
        
        # Device setup
        self.device = self._get_device()
    
    def _get_device(self) -> torch.device:
        """Get appropriate device."""
        if self.config.device == "auto":
            return torch.device("cuda" if torch.cuda.is_available() else "cpu")
        return torch.device(self.config.device)
    def discretize_state(self, state: np.ndarray) -> int:
        """Convert continuous state to discrete state index."""
        # Normalize state to [0, 1] range
        normalized_state = np.clip(state, 0, 1)
        
        # Limit state dimensions for discretization
        limited_state = normalized_state[:min(len(normalized_state), 6)]
        
        # Convert to discrete bins
        discrete_indices = (limited_state * (self.state_bins - 1)).astype(int)
        
        # Convert multi-dimensional index to single index
        state_index = 0
        for i, idx in enumerate(discrete_indices):
            state_index += idx * (self.state_bins ** i)
        
        return int(state_index)  # Ensure return type is int
    
    def select_action(self, state: np.ndarray) -> int:
        """Select action using epsilon-greedy policy."""
        if np.random.random() < self.epsilon:
            # Exploration: random action
            return np.random.randint(0, self.config.action_dim)
        else:
            # Exploitation: best action
            state_idx = self.discretize_state(state)
            
            if self.use_sparse:
                q_values = [self.q_table.get((state_idx, a), 0.0)
                           for a in range(self.config.action_dim)]
                return int(np.argmax(q_values))
            else:
                return int(np.argmax(self.q_table[state_idx, :]))
    
    def update_q_value(self, state: np.ndarray, action: int, reward: float,
                      next_state: np.ndarray, done: bool):
        """Update Q-value using Q-learning update rule."""
        state_idx = self.discretize_state(state)
        next_state_idx = self.discretize_state(next_state)
        
        # Current Q-value
        if self.use_sparse:
            current_q = self.q_table.get((state_idx, action), 0.0)
        else:
            current_q = self.q_table[state_idx, action]
        
        # Maximum Q-value for next state
        if done:
            max_next_q = 0
        else:
            if self.use_sparse:
                next_q_values = [self.q_table.get((next_state_idx, a), 0.0)
                               for a in range(self.config.action_dim)]
                max_next_q = max(next_q_values)
            else:
                max_next_q = np.max(self.q_table[next_state_idx, :])
        
        # Q-learning update
        target_q = reward + self.config.discount_factor * max_next_q
        new_q = current_q + self.learning_rate * (target_q - current_q)
        
        if self.use_sparse:
            self.q_table[(state_idx, action)] = new_q
        else:
            self.q_table[state_idx, action] = new_q
    
    def get_q_value(self, state: np.ndarray, action: int) -> float:
        """Get Q-value for state-action pair."""
        state_idx = self.discretize_state(state)
        
        if self.use_sparse:
            return self.q_table.get((state_idx, action), 0.0)
        else:
            return self.q_table[state_idx, action]
    
    def decay_epsilon(self):
        """Decay exploration rate."""
        self.epsilon = max(self.config.epsilon_end, self.epsilon * self.config.epsilon_decay)


class PolicyGradientAgent:
    """Policy Gradient agent implementation."""
    
    def __init__(self, config: RLConfig):
        self.config = config
        self.device = self._get_device()
        
        # Initialize policy network
        self.policy_network = PolicyNetwork(
            config.state_dim, 
            config.action_dim, 
            config.hidden_dim, 
            config.num_layers
        ).to(self.device)
        
        self.optimizer = optim.Adam(self.policy_network.parameters(), lr=config.learning_rate)
        
        # Episode storage
        self.episode_states = []
        self.episode_actions = []
        self.episode_rewards = []
    
    def _get_device(self) -> torch.device:
        """Get appropriate device."""
        if self.config.device == "auto":
            return torch.device("cuda" if torch.cuda.is_available() else "cpu")
        return torch.device(self.config.device)
    
    def select_action(self, state: np.ndarray) -> int:
        """Select action from policy distribution."""
        state_tensor = torch.FloatTensor(state).unsqueeze(0).to(self.device)
        
        with torch.no_grad():
            action_probs = self.policy_network(state_tensor)
            action_dist = torch.distributions.Categorical(action_probs)
            action = action_dist.sample()
        
        return action.item()
    
    def update_policy(self, states: List[np.ndarray], actions: List[int], rewards: List[float]):
        """Update policy using policy gradient."""
        # Calculate returns (discounted rewards)
        returns = self.calculate_advantages(rewards)
        
        # Convert to tensors
        states_tensor = torch.FloatTensor(np.array(states)).to(self.device)
        actions_tensor = torch.LongTensor(actions).to(self.device)
        returns_tensor = torch.FloatTensor(returns).to(self.device)
        
        # Calculate policy loss
        action_probs = self.policy_network(states_tensor)
        action_dist = torch.distributions.Categorical(action_probs)
        log_probs = action_dist.log_prob(actions_tensor)
        
        # Policy gradient loss
        policy_loss = -(log_probs * returns_tensor).mean()
        
        # Update policy
        self.optimizer.zero_grad()
        policy_loss.backward()
        self.optimizer.step()
    
    def calculate_advantages(self, rewards: List[float]) -> List[float]:
        """Calculate advantages (returns) for policy gradient."""
        returns = []
        discounted_sum = 0
        
        # Calculate discounted returns
        for reward in reversed(rewards):
            discounted_sum = reward + self.config.discount_factor * discounted_sum
            returns.insert(0, discounted_sum)
        
        # Normalize returns
        returns = np.array(returns)
        if len(returns) > 1:
            returns = (returns - np.mean(returns)) / (np.std(returns) + 1e-8)
        
        return returns.tolist()


class MultiAgentCoordinator:
    """Coordinator for multi-agent RL systems."""
    
    def __init__(self, num_agents: int, agent_configs: List[RLConfig]):
        self.num_agents = num_agents
        self.agents = []
        self.communication_enabled = True
        
        # Create agents
        for config in agent_configs:
            if config.algorithm == "q_learning":
                agent = QLearningAgent(config)
            elif config.algorithm == "policy_gradient":
                agent = PolicyGradientAgent(config)
            else:
                agent = QLearningAgent(config)  # Default to Q-learning
            self.agents.append(agent)
    
    async def get_coordinated_actions(self, states: List[np.ndarray]) -> List[int]:
        """Get coordinated actions from all agents."""
        actions = []
        
        for i, (agent, state) in enumerate(zip(self.agents, states)):
            action = agent.select_action(state)
            actions.append(action)
        
        # Apply coordination if enabled
        if self.communication_enabled:
            actions = self._coordinate_actions(actions, states)
        
        return actions
    
    def _coordinate_actions(self, actions: List[int], states: List[np.ndarray]) -> List[int]:
        """Apply coordination logic to actions."""
        # Simple coordination: avoid conflicts
        coordinated_actions = actions.copy()
        
        # Check for action conflicts and resolve
        action_counts = {}
        for action in actions:
            action_counts[action] = action_counts.get(action, 0) + 1
        
        # If multiple agents choose same action, randomize some
        for action, count in action_counts.items():
            if count > 1:
                # Find agents with this action
                agent_indices = [i for i, a in enumerate(actions) if a == action]
                # Keep first agent, randomize others
                for i in agent_indices[1:]:
                    coordinated_actions[i] = np.random.randint(0, self.agents[i].config.action_dim)
        
        return coordinated_actions


class StrategyOptimizer:
    """Optimizer for RL strategies."""
    
    def __init__(self, rl_optimizer):
        self.rl_optimizer = rl_optimizer
        self.strategy_history = []
    
    def optimize_strategy(self, strategies: List[OptimizationStrategy]) -> OptimizationStrategy:
        """Optimize strategy selection."""
        best_strategy = None
        best_score = -float('inf')
        
        for strategy in strategies:
            score = self._evaluate_strategy(strategy)
            strategy.performance_score = score
            
            if score > best_score:
                best_score = score
                best_strategy = strategy
        
        self.strategy_history.append(best_strategy)
        return best_strategy
    
    def _evaluate_strategy(self, strategy: OptimizationStrategy) -> float:
        """Evaluate strategy performance."""
        # Mock evaluation - in real implementation, this would run trials
        base_score = 0.5
        
        # Adjust based on strategy parameters
        params = strategy.parameters
        
        if "exploration_rate" in params:
            exploration_rate = params["exploration_rate"]
            if 0.1 <= exploration_rate <= 0.3:
                base_score += 0.2
        
        if "learning_rate" in params:
            learning_rate = params["learning_rate"]
            if 1e-4 <= learning_rate <= 1e-2:
                base_score += 0.2
        
        # Add some randomness
        base_score += np.random.uniform(-0.1, 0.1)
        
        return max(0.0, min(1.0, base_score))


class RLOptimizer:
    """Main Reinforcement Learning Optimizer class."""
    
    def __init__(self, config: RLConfig):
        self.config = config
        self.total_episodes = 0
        self.total_steps = 0
        
        # Initialize components
        self.experience_replay = ExperienceReplay(config.memory_size)
        self.reward_function = RewardFunction()
        
        # Initialize agent based on algorithm
        if config.algorithm == "q_learning":
            self.agent = QLearningAgent(config)
        elif config.algorithm == "policy_gradient":
            self.agent = PolicyGradientAgent(config)
        else:
            self.agent = QLearningAgent(config)  # Default to Q-learning
        
        # Performance tracking
        self.episode_rewards = []
        self.episode_steps_list = []
        self.performance_metrics = PerformanceMetrics()
        
        # Curriculum learning
        self.curriculum = []
        self.current_curriculum_stage = 0
        
        logging.info(f"RLOptimizer initialized with algorithm: {config.algorithm}")
    
    async def train_episode(self, environment) -> Tuple[float, int]:
        """Train for a single episode."""
        state = environment.reset()
        episode_reward = 0.0
        episode_steps = 0
        
        while True:
            # Select action
            action = self.agent.select_action(state)
            
            # Take action in environment
            next_state, reward, done, info = environment.step(action)
            
            # Store experience
            experience = Experience(
                state=state.copy(),
                action=action,
                reward=reward,
                next_state=next_state.copy(),
                done=done
            )
            self.experience_replay.store(experience)
            
            # Update agent
            if hasattr(self.agent, 'update_q_value'):
                self.agent.update_q_value(state, action, reward, next_state, done)
            
            # Update for next step
            state = next_state
            episode_reward += reward
            episode_steps += 1
            
            if done:
                break
        
        # Decay exploration
        if hasattr(self.agent, 'decay_epsilon'):
            self.agent.decay_epsilon()
        
        # Update counters
        self.total_episodes += 1
        self.total_steps += episode_steps
        
        # Record metrics
        self._record_episode_metrics(episode_reward, episode_steps)
        
        return episode_reward, episode_steps
    
    async def train_multiple_episodes(self, environment, num_episodes: int) -> Dict[str, Any]:
        """Train for multiple episodes."""
        episode_rewards = []
        episode_steps = []
        
        for episode in range(num_episodes):
            reward, steps = await self.train_episode(environment)
            episode_rewards.append(reward)
            episode_steps.append(steps)
        
        return {
            "episode_rewards": episode_rewards,
            "episode_steps": episode_steps,
            "total_episodes": self.total_episodes,
            "total_steps": self.total_steps
        }
    
    def _record_episode_metrics(self, reward: float, steps: int):
        """Record episode metrics for performance tracking."""
        self.episode_rewards.append(reward)
        self.episode_steps_list.append(steps)
        
        # Keep only recent episodes for metrics
        max_history = 100
        if len(self.episode_rewards) > max_history:
            self.episode_rewards = self.episode_rewards[-max_history:]
            self.episode_steps_list = self.episode_steps_list[-max_history:]
    
    def get_performance_metrics(self) -> Dict[str, Any]:
        """Get current performance metrics."""
        if not self.episode_rewards:
            return {
                "average_reward": 0.0,
                "reward_trend": 0.0,
                "average_steps": 0.0,
                "convergence_rate": 0.0,
                "exploration_rate": getattr(self.agent, 'epsilon', 0.0)
            }
        
        # Calculate metrics
        avg_reward = np.mean(self.episode_rewards)
        avg_steps = np.mean(self.episode_steps_list)
        
        # Calculate trend (improvement over time)
        if len(self.episode_rewards) >= 10:
            recent_rewards = self.episode_rewards[-10:]
            if len(self.episode_rewards) >= 20:
                older_rewards = self.episode_rewards[-20:-10]
            elif len(self.episode_rewards) > 10:
                older_rewards = self.episode_rewards[:-10]
            else:
                older_rewards = []
            
            if older_rewards:
                reward_trend = np.mean(recent_rewards) - np.mean(older_rewards)
            else:
                # If no older rewards, calculate trend from first half vs second half
                mid_point = len(self.episode_rewards) // 2
                first_half = self.episode_rewards[:mid_point]
                second_half = self.episode_rewards[mid_point:]
                if first_half and second_half:
                    reward_trend = np.mean(second_half) - np.mean(first_half)
                else:
                    reward_trend = 0.0
        else:
            reward_trend = 0.0
        
        # Calculate convergence rate (stability of recent performance)
        if len(self.episode_rewards) >= 10:
            recent_std = np.std(self.episode_rewards[-10:])
            convergence_rate = max(0.0, 1.0 - recent_std)
        else:
            convergence_rate = 0.0
        
        return {
            "average_reward": avg_reward,
            "reward_trend": reward_trend,
            "average_steps": avg_steps,
            "convergence_rate": convergence_rate,
            "exploration_rate": getattr(self.agent, 'epsilon', 0.0)
        }
    
    def save_model(self, filepath: str):
        """Save model to file."""
        model_data = {
            "config": self.config,
            "total_episodes": self.total_episodes,
            "total_steps": self.total_steps,
            "episode_rewards": self.episode_rewards,
            "episode_steps": self.episode_steps_list
        }
        
        # Save agent-specific data
        if hasattr(self.agent, 'q_table'):
            model_data["q_table"] = self.agent.q_table
        elif hasattr(self.agent, 'policy_network'):
            model_data["policy_state_dict"] = self.agent.policy_network.state_dict()
        
        with open(filepath, 'wb') as f:
            pickle.dump(model_data, f)
    
    def load_model(self, filepath: str):
        """Load model from file."""
        with open(filepath, 'rb') as f:
            model_data = pickle.load(f)
        
        # Restore basic data
        self.total_episodes = model_data.get("total_episodes", 0)
        self.total_steps = model_data.get("total_steps", 0)
        self.episode_rewards = model_data.get("episode_rewards", [])
        self.episode_steps_list = model_data.get("episode_steps", [])
        
        # Restore agent-specific data
        if "q_table" in model_data and hasattr(self.agent, 'q_table'):
            self.agent.q_table = model_data["q_table"]
        elif "policy_state_dict" in model_data and hasattr(self.agent, 'policy_network'):
            self.agent.policy_network.load_state_dict(model_data["policy_state_dict"])
    
    def _update_adaptive_learning_rate(self, reward: float):
        """Update learning rate based on performance."""
        if not hasattr(self.agent, 'learning_rate'):
            return
        
        # Simple adaptive learning rate
        if len(self.episode_rewards) >= 5:
            recent_avg = np.mean(self.episode_rewards[-5:])
            if recent_avg < 0:  # Poor performance
                self.agent.learning_rate = min(self.agent.learning_rate * 1.1, 0.01)
            else:  # Good performance
                self.agent.learning_rate = max(self.agent.learning_rate * 0.95, 1e-5)
    
    def set_curriculum(self, curriculum: List[Dict[str, Any]]):
        """Set curriculum for progressive learning."""
        self.curriculum = curriculum
        self.current_curriculum_stage = 0
    
    def get_current_curriculum_stage(self) -> Dict[str, Any]:
        """Get current curriculum stage."""
        if not self.curriculum:
            return {}
        return self.curriculum[self.current_curriculum_stage]
    
    def _advance_curriculum_stage(self):
        """Advance to next curriculum stage."""
        if self.current_curriculum_stage < len(self.curriculum) - 1:
            self.current_curriculum_stage += 1


# Additional classes for comprehensive implementation

class RLOptimizerConfig:
    """Configuration dataclass for RL training."""
    pass  # Alias for RLConfig


class PolicyModel:
    """Base class for different policy implementations."""
    pass  # Implemented via PolicyNetwork and agents


class RLEnvironment:
    """SAFLA-specific environment implementations."""
    pass  # To be implemented based on specific SAFLA domains


class TrainingPipeline:
    """End-to-end training orchestration."""
    pass  # To be implemented for full pipeline


class ExplorationStrategy:
    """Various exploration algorithms."""
    pass  # Implemented via epsilon-greedy and other strategies