defmodule Automaton.Types.DECPOMDP do
  @moduledoc """
  Implements the Decentralized Partially Observable Markov Decision Process
  (DEC-POMDP) state space representation for multi-agent control and prediction.
  
  DECPOMDP's can be defined with the tuple: { I, S, {A_i}, T, R, {Omega_i}, O, h }
    • I, a finite set of agents
    • S, a finite set of states with designated initial distribution b^0
    • A_i, each agents finite set of actions
    • T, state transition model P(s'|s,a->). Computes pdf of the updated states,
      depends on all agents
    • R, the reward model, depends on all agents
    • Omega_i, each agents finite set of observations
    • O, the observation model: P(o|s',a->), depends on all agents
    • h, horizon or discount factor
    
  This implementation follows a factored approach, where:
  1. The full state space is factored into local state variables
  2. The transition model uses Dynamic Bayesian Networks to represent dependencies
  3. Policies are computed using value-based methods with belief state tracking
  4. Coordination is achieved through shared reward structures
  """
  alias Automaton.Types.DECPOMDP.Config.Parser
  alias Automaton.Blackboard
  
  defmodule State do
    @moduledoc false
    defstruct [
      # Environment state
      global_state: %{},      # Shared global state variables
      agent_states: %{},      # Map of agent_id -> local state
      state_space: [],        # Definition of possible states
      
      # Agent information
      agents: [],             # List of agent IDs
      agent_actions: %{},     # Map of agent_id -> available actions
      agent_observations: %{}, # Map of agent_id -> observation space
      
      # Model parameters
      transition_model: nil,  # Function for P(s'|s,a)
      observation_model: nil, # Function for P(o|s',a)
      reward_model: nil,      # Function for R(s,a,s')
      
      # Policy and learning
      joint_policy: %{},      # Current joint policy
      belief_states: %{},     # Map of agent_id -> belief state
      value_functions: %{},   # Map of agent_id -> value function
      
      # Configuration
      discount_factor: 0.95,  # Gamma parameter for discounting future rewards
      horizon: 100,           # Planning horizon (infinite if 0)
      learning_rate: 0.1,     # Alpha parameter for value function updates
      
      # Execution state
      iteration: 0,           # Current iteration count
      total_reward: 0.0,      # Accumulated reward
      is_terminal: false      # Whether current state is terminal
    ]
  end
  
  defmacro __using__(opts) do
    quote do
      use GenServer
      alias Automaton.Types.DECPOMDP
      alias Automaton.Types.DECPOMDP.State
      alias Automaton.Blackboard
      
      # Client API
      
      def start_link(args) do
        GenServer.start_link(__MODULE__, args, name: __MODULE__)
      end
      
      # Initialize the DECPOMDP model
      def init(args) do
        decpomdp_config = unquote(opts)
        parser_output = DECPOMDP.Config.Parser.call(decpomdp_config)
        
        {:ok, initial_state} = initialize_state(parser_output, args)
        
        # Schedule first iteration
        if Map.get(args, :auto_start, true) do
          Process.send_after(self(), :iterate, 0)
        end
        
        {:ok, initial_state}
      end
      
      # Server callbacks
      
      def handle_info(:iterate, state) do
        if state.is_terminal do
          {:noreply, state}
        else
          # Perform one iteration of the algorithm
          updated_state = iterate(state)
          
          # Schedule next iteration
          unless updated_state.is_terminal do
            Process.send_after(self(), :iterate, 100)  # Adjust timing as needed
          end
          
          {:noreply, updated_state}
        end
      end
      
      def handle_call(:get_state, _from, state) do
        {:reply, state, state}
      end
      
      def handle_call({:get_policy, agent_id}, _from, state) do
        policy = Map.get(state.joint_policy, agent_id, %{})
        {:reply, policy, state}
      end
      
      def handle_call({:update_observation, agent_id, observation}, _from, state) do
        # Update the belief state based on new observation
        updated_state = update_belief_state(state, agent_id, observation)
        {:reply, :ok, updated_state}
      end
      
      def handle_call({:select_action, agent_id}, _from, state) do
        # Select an action based on current policy and belief state
        {action, updated_state} = select_action(state, agent_id)
        {:reply, action, updated_state}
      end
      
      def handle_cast({:update_reward, reward}, state) do
        # Update value functions based on received reward
        updated_state = update_value_functions(state, reward)
        {:noreply, updated_state}
      end
      
      # Implementation functions
      
      # Initialize the DECPOMDP state from configuration
      def initialize_state(parser_output, args) do
        # Extract configuration parameters
        {agents, actions, observations, states, transition, observation, reward} = parser_output
        
        # Setup initial state
        initial_state = %State{
          agents: agents,
          agent_actions: actions,
          agent_observations: observations,
          state_space: states,
          transition_model: transition,
          observation_model: observation,
          reward_model: reward,
          discount_factor: Map.get(args, :discount_factor, 0.95),
          horizon: Map.get(args, :horizon, 100),
          learning_rate: Map.get(args, :learning_rate, 0.1)
        }
        
        # Initialize belief states for all agents
        belief_states = Enum.reduce(agents, %{}, fn agent_id, acc ->
          # Start with uniform distribution over states
          initial_belief = Enum.reduce(states, %{}, fn state, belief_acc ->
            Map.put(belief_acc, state, 1.0 / length(states))
          end)
          
          Map.put(acc, agent_id, initial_belief)
        end)
        
        # Initialize empty policies for all agents
        joint_policy = Enum.reduce(agents, %{}, fn agent_id, acc ->
          Map.put(acc, agent_id, %{})
        end)
        
        # Initialize value functions for all agents
        value_functions = Enum.reduce(agents, %{}, fn agent_id, acc ->
          # For each agent, create a value function for each belief state
          initial_values = Enum.reduce(states, %{}, fn state, value_acc ->
            Map.put(value_acc, state, 0.0)
          end)
          
          Map.put(acc, agent_id, initial_values)
        end)
        
        final_state = %{initial_state | 
          belief_states: belief_states,
          joint_policy: joint_policy,
          value_functions: value_functions
        }
        
        {:ok, final_state}
      end
      
      # Perform one iteration of the DECPOMDP algorithm
      def iterate(state) do
        # 1. For each agent, select an action based on current policy
        {actions, state_after_selection} = Enum.reduce(state.agents, {%{}, state}, 
          fn agent_id, {actions_acc, state_acc} ->
            {action, updated_state} = select_action(state_acc, agent_id)
            {Map.put(actions_acc, agent_id, action), updated_state}
          end)
        
        # 2. Apply joint action to environment (simulate or real)
        # In a real system, this would interact with the actual environment
        {next_global_state, observations, reward} = apply_joint_action(
          state_after_selection.global_state, 
          actions
        )
        
        # 3. Update belief states based on observations
        state_after_observations = Enum.reduce(observations, state_after_selection,
          fn {agent_id, observation}, acc_state ->
            update_belief_state(acc_state, agent_id, observation)
          end)
        
        # 4. Update value functions based on reward
        state_after_value_update = update_value_functions(state_after_observations, reward)
        
        # 5. Update policies based on new value functions
        state_after_policy_update = update_policies(state_after_value_update)
        
        # 6. Check if terminal state reached
        is_terminal = check_terminal(state_after_policy_update)
        
        # 7. Return updated state
        %{state_after_policy_update | 
          global_state: next_global_state,
          iteration: state.iteration + 1,
          total_reward: state.total_reward + reward,
          is_terminal: is_terminal
        }
      end
      
      # Select an action for an agent based on current policy and belief state
      def select_action(state, agent_id) do
        belief = Map.get(state.belief_states, agent_id, %{})
        policy = Map.get(state.joint_policy, agent_id, %{})
        
        # If we have a policy mapping for this belief state, use it
        # Otherwise, select action with highest expected value
        action = case find_policy_action(policy, belief) do
          nil ->
            # No policy entry - select best action based on value function
            select_best_action(state, agent_id, belief)
            
          defined_action ->
            # Policy defines an action - use it
            defined_action
        end
        
        {action, state}
      end
      
      # Find a matching policy action for the current belief state
      def find_policy_action(policy, belief) do
        # In a real impl, would use a more sophisticated belief state matching
        # For now, just use the most likely state as the key
        {most_likely_state, _probability} = Enum.max_by(belief, fn {_state, prob} -> prob end)
        Map.get(policy, most_likely_state)
      end
      
      # Select the best action based on value function
      def select_best_action(state, agent_id, belief) do
        value_function = Map.get(state.value_functions, agent_id, %{})
        available_actions = Map.get(state.agent_actions, agent_id, [])
        
        # Calculate expected value for each action
        action_values = Enum.map(available_actions, fn action ->
          expected_value = calculate_expected_value(state, agent_id, action, belief, value_function)
          {action, expected_value}
        end)
        
        # Select action with highest expected value
        # Add random tie-breaking if multiple actions have the same value
        {best_action, _value} = Enum.max_by(action_values, fn {_action, value} -> value end)
        best_action
      end
      
      # Calculate expected value of an action given belief state
      def calculate_expected_value(state, agent_id, action, belief, value_function) do
        # Sum over all possible states, weighted by belief probability
        Enum.reduce(belief, 0.0, fn {state_key, probability}, acc ->
          state_value = Map.get(value_function, state_key, 0.0)
          acc + probability * state_value
        end)
      end
      
      # Apply joint action to the environment
      def apply_joint_action(global_state, actions) do
        # In a real implementation, this would interact with the actual environment
        # For this example, we'll simulate a simple transition
        
        # Calculate next state based on transition model
        next_state = apply_transition_model(global_state, actions)
        
        # Calculate observations for each agent
        observations = Enum.reduce(actions, %{}, fn {agent_id, _action}, acc ->
          observation = generate_observation(next_state, agent_id)
          Map.put(acc, agent_id, observation)
        end)
        
        # Calculate reward
        reward = calculate_reward(global_state, actions, next_state)
        
        {next_state, observations, reward}
      end
      
      # Apply transition model to get next state
      def apply_transition_model(state, actions) do
        # Example implementation - should be replaced with actual model
        # In a real system, this would use the transition_model function
        # For this example, just simulate a random next state
        state
      end
      
      # Generate an observation for an agent
      def generate_observation(state, agent_id) do
        # Example implementation - should be replaced with actual model
        # In a real system, this would use the observation_model function
        %{position: {0, 0}, visible_agents: []}
      end
      
      # Calculate reward for a state-action-state transition
      def calculate_reward(state, actions, next_state) do
        # Example implementation - should be replaced with actual model
        # In a real system, this would use the reward_model function
        
        # For example, reward could be based on task completion or goal proximity
        1.0
      end
      
      # Update the belief state based on a new observation
      def update_belief_state(state, agent_id, observation) do
        current_belief = Map.get(state.belief_states, agent_id, %{})
        
        # Apply Bayes rule to update belief state
        updated_belief = bayes_belief_update(
          current_belief, 
          observation, 
          state.observation_model,
          state.state_space
        )
        
        %{state | belief_states: Map.put(state.belief_states, agent_id, updated_belief)}
      end
      
      # Bayesian belief update based on observation
      def bayes_belief_update(belief, observation, observation_model, state_space) do
        # In a full implementation, this would apply Bayes rule:
        # b'(s') = n * O(o|s',a) * sum_s[ T(s'|s,a) * b(s) ]
        # where n is a normalization factor
        
        # For this example, simplify with a basic update
        # In a real system, implement the full Bayesian update
        
        # Example: Increase probability of states consistent with observation
        updated = Enum.reduce(belief, %{}, fn {state_key, prob}, acc ->
          # Adjust probability based on observation match
          adjusted_prob = if observation_matches(state_key, observation) do
            prob * 1.2  # Increase probability for matching states
          else
            prob * 0.8  # Decrease probability for non-matching states
          end
          
          Map.put(acc, state_key, adjusted_prob)
        end)
        
        # Normalize probabilities to sum to 1.0
        normalize_belief(updated)
      end
      
      # Check if a state is consistent with an observation
      def observation_matches(state, observation) do
        # Example implementation - replace with actual matching logic
        true
      end
      
      # Normalize belief state so probabilities sum to 1.0
      def normalize_belief(belief) do
        total = Enum.reduce(belief, 0.0, fn {_state, prob}, acc -> acc + prob end)
        
        if total > 0.0 do
          Enum.reduce(belief, %{}, fn {state, prob}, acc ->
            Map.put(acc, state, prob / total)
          end)
        else
          # If all probabilities are 0, return uniform distribution
          uniform_prob = 1.0 / map_size(belief)
          Enum.reduce(belief, %{}, fn {state, _prob}, acc ->
            Map.put(acc, state, uniform_prob)
          end)
        end
      end
      
      # Update value functions based on observed reward
      def update_value_functions(state, reward) do
        # For each agent, update their value function
        updated_values = Enum.reduce(state.agents, state.value_functions, fn agent_id, acc_values ->
          current_values = Map.get(acc_values, agent_id, %{})
          belief = Map.get(state.belief_states, agent_id, %{})
          
          # Update values for all states in belief, weighted by belief probability
          updated_agent_values = Enum.reduce(belief, current_values, fn {state_key, prob}, acc ->
            current = Map.get(acc, state_key, 0.0)
            # Q-learning like update, weighted by belief probability
            new_value = current + state.learning_rate * prob * (reward - current)
            Map.put(acc, state_key, new_value)
          end)
          
          Map.put(acc_values, agent_id, updated_agent_values)
        end)
        
        %{state | value_functions: updated_values}
      end
      
      # Update policies based on new value functions
      def update_policies(state) do
        # For each agent, update their policy
        updated_policies = Enum.reduce(state.agents, state.joint_policy, fn agent_id, acc_policies ->
          current_policy = Map.get(acc_policies, agent_id, %{})
          value_function = Map.get(state.value_functions, agent_id, %{})
          available_actions = Map.get(state.agent_actions, agent_id, [])
          
          # For each state, select the action with highest expected value
          updated_agent_policy = Enum.reduce(state.state_space, current_policy, fn state_key, acc ->
            # Create a temporary belief concentrated on this state
            single_state_belief = %{state_key => 1.0}
            
            # Find best action for this state
            best_action = Enum.max_by(available_actions, fn action ->
              calculate_expected_value(state, agent_id, action, single_state_belief, value_function)
            end)
            
            Map.put(acc, state_key, best_action)
          end)
          
          Map.put(acc_policies, agent_id, updated_agent_policy)
        end)
        
        %{state | joint_policy: updated_policies}
      end
      
      # Check if the current state is terminal
      def check_terminal(state) do
        # In a real implementation, define terminal conditions like:
        # - Goal achieved
        # - Maximum iterations reached
        # - Convergence achieved
        
        # For this example, just check if max iterations reached
        state.iteration >= state.horizon
      end
      
      # Integration with behavior tree
      
      # Select an action based on DECPOMDP policy for use in behavior tree
      def bt_select_action(decpomdp_pid, agent_id, observation) do
        # Update DECPOMDP with new observation
        GenServer.call(decpomdp_pid, {:update_observation, agent_id, observation})
        
        # Get action from policy
        GenServer.call(decpomdp_pid, {:select_action, agent_id})
      end
      
      # Update DECPOMDP with reward from behavior tree execution
      def bt_update_reward(decpomdp_pid, reward) do
        GenServer.cast(decpomdp_pid, {:update_reward, reward})
      end
      
      # Load DECPOMDP state from blackboard for persistence
      def load_state_from_blackboard(state, blackboard_pid, node_id) do
        case Automaton.Blackboard.get_persistent(blackboard_pid, "decpomdp_#{node_id}", "state") do
          {:ok, nil} -> state
          {:ok, saved_state} -> saved_state
          {:error, _} -> state
        end
      end
      
      # Save DECPOMDP state to blackboard for persistence
      def save_state_to_blackboard(state, blackboard_pid, node_id) do
        Automaton.Blackboard.store_persistent(
          blackboard_pid,
          "decpomdp_#{node_id}",
          "state",
          state
        )
        
        state
      end
    end
  end
end