package agents.MCTS

import ggi.Action
import java.lang.AssertionError

data class MCTSParameters(
        val C: Double = 1.0,
        val selectionMethod: MCTSSelectionMethod = MCTSSelectionMethod.SIMPLE,
        val maxPlayouts: Int = 100,
        val timeLimit: Int = 1000,
        val horizon: Int = 2000,     // limit of actions taken across both tree and rollout policies
        val discountRate: Double = 1.0,
        val pruneTree: Boolean = false,
        val maxDepth: Int = 100,
        val maxActions: Int = 20,
        val actionFilter: String = "none"
)

enum class MCTSSelectionMethod { ROBUST, SIMPLE }

class MCStatistics(
        val params: MCTSParameters = MCTSParameters(),
        var visitCount: Int = 0,
        var validVisitCount: Int = 0,
        var sum: Double = 0.0,
        var max: Double = Double.NEGATIVE_INFINITY,
        var min: Double = Double.POSITIVE_INFINITY,
        var sumSquares: Double = 0.0
) {
    val mean: Double
        get() = if (visitCount == 0) Double.NaN else sum / visitCount

    fun UCTScore(): Double {
        return if (visitCount == 0)
            Double.POSITIVE_INFINITY
        else
            mean + params.C * Math.sqrt(Math.log(validVisitCount.toDouble()) / visitCount)
    }
}

class TTNode(
        val params: MCTSParameters = MCTSParameters(),
        val actions: List<Action>
) {
    val actionMap: Map<Action, MCStatistics> = actions.associateWith { MCStatistics() }

    fun hasUnexploredActions(): Boolean {
        return actionMap.values.any { it.visitCount == 0 }
    }

    fun getRandomUnexploredAction(validOptions: List<Action>): Action {
        if ((validOptions - actions).isNotEmpty()) {
            TODO("Need to cater for previously unknown options")
        }
        val filteredOptions = validOptions.filter { actionMap[it]!!.visitCount == 0 }
        return if (filteredOptions.isEmpty()) {
            throw AssertionError("No unexplored options to choose from")
        } else filteredOptions.random()
    }

    fun getUCTAction(validOptions: List<Action>): Action {
        val withScores = validOptions.map { a -> a to (actionMap[a]?.UCTScore() ?: 0.0) }
        return withScores.maxBy { (_, score) -> score }!!.first
    }

    fun getBestAction(): Action {
        val chosenAction = actionMap.maxBy {
            when (params.selectionMethod) {
                MCTSSelectionMethod.SIMPLE -> when (it.value.visitCount) {
                    0 -> 0.0
                    else -> it.value.mean
                }
                MCTSSelectionMethod.ROBUST -> it.value.visitCount.toDouble()
            }
        }?.key ?: throw AssertionError("Unexpected")
        return chosenAction
    }

    fun update(action: Action, possibleActions: List<Action>, reward: Double) {
        actionMap.filter { (k, _) -> possibleActions.contains(k) }
                .mapNotNull { (_, v) -> v }
                .forEach { it.validVisitCount++ }
        val stats = actionMap[action]
        if (stats != null) {
            stats.visitCount++
            stats.sum += reward
            stats.sumSquares += reward * reward
            if (stats.max < reward) stats.max = reward
            if (stats.min > reward) stats.min = reward
        }
    }

}

class OLNode(
        val key: Long,
        val actionMap: Map<Action, MCStatistics> = mutableMapOf(),
        val treeMap: Map<Action, OLNode> = mutableMapOf()
)
package agents.MCTS

import ggi.Action
import java.lang.AssertionError

data class MCTSParameters(
        val C: Double = 1.0,
        val selectionMethod: MCTSSelectionMethod = MCTSSelectionMethod.SIMPLE,
        val maxPlayouts: Int = 100,
        val timeLimit: Int = 1000,
        val horizon: Int = 2000,     // limit of actions taken across both tree and rollout policies
        val discountRate: Double = 1.0,
        val pruneTree: Boolean = false,
        val maxDepth: Int = 100,
        val maxActions: Int = 20,
        val actionFilter: String = "none"
)

enum class MCTSSelectionMethod { ROBUST, SIMPLE }

class MCStatistics(
        val params: MCTSParameters = MCTSParameters(),
        var visitCount: Int = 0,
        var validVisitCount: Int = 0,
        var sum: Double = 0.0,
        var max: Double = Double.NEGATIVE_INFINITY,
        var min: Double = Double.POSITIVE_INFINITY,
        var sumSquares: Double = 0.0
) {
    val mean: Double
        get() = if (visitCount == 0) Double.NaN else sum / visitCount

    fun UCTScore(): Double {
        return if (visitCount == 0)
            Double.POSITIVE_INFINITY
        else
            mean + params.C * Math.sqrt(Math.log(validVisitCount.toDouble()) / visitCount)
    }
}

class TTNode(
        val params: MCTSParameters = MCTSParameters(),
        val actions: List<Action>
) {
    val actionMap: Map<Action, MCStatistics> = actions.associateWith { MCStatistics() }

    fun hasUnexploredActions(): Boolean {
        return actionMap.values.any { it.visitCount == 0 }
    }

    fun getRandomUnexploredAction(validOptions: List<Action>): Action {
        if ((validOptions - actions).isNotEmpty()) {
            TODO("Need to cater for previously unknown options")
        }
        val filteredOptions = validOptions.filter { actionMap[it]!!.visitCount == 0 }
        return if (filteredOptions.isEmpty()) {
            throw AssertionError("No unexplored options to choose from")
        } else filteredOptions.random()
    }

    fun getUCTAction(validOptions: List<Action>): Action {
        val withScores = validOptions.map { a -> a to (actionMap[a]?.UCTScore() ?: 0.0) }
        return withScores.maxBy { (_, score) -> score }!!.first
    }

    fun getBestAction(): Action {
        val chosenAction = actionMap.maxBy {
            when (params.selectionMethod) {
                MCTSSelectionMethod.SIMPLE -> when (it.value.visitCount) {
                    0 -> 0.0
                    else -> it.value.mean
                }
                MCTSSelectionMethod.ROBUST -> it.value.visitCount.toDouble()
            }
        }?.key ?: throw AssertionError("Unexpected")
        return chosenAction
    }

    fun update(action: Action, possibleActions: List<Action>, reward: Double) {
        actionMap.filter { (k, _) -> possibleActions.contains(k) }
                .mapNotNull { (_, v) -> v }
                .forEach { it.validVisitCount++ }
        val stats = actionMap[action]
        if (stats != null) {
            stats.visitCount++
            stats.sum += reward
            stats.sumSquares += reward * reward
            if (stats.max < reward) stats.max = reward
            if (stats.min > reward) stats.min = reward
        }
    }

}

class OLNode(
        val key: Long,
        val actionMap: Map<Action, MCStatistics> = mutableMapOf(),
        val treeMap: Map<Action, OLNode> = mutableMapOf()
)