"""
Efficiency & Optimization Reward Components

Rewards for optimal play, routing, and game knowledge.
"""

from typing import Any, Dict

from synth_ai.environments.environment.rewards.core import RewardComponent


class FastTravelUsageReward(RewardComponent):
    """Reward for using Fly effectively - +10 points"""

    async def score(self, state: Dict[str, Any], action: Dict[str, Any]) -> float:
        # Placeholder - would detect Fly usage
        return 0.0


class OptimalRoutingReward(RewardComponent):
    """Reward for taking efficient paths - +15 points"""

    async def score(self, state: Dict[str, Any], action: Dict[str, Any]) -> float:
        # Placeholder - would analyze path efficiency
        return 0.0


class PuzzleSolvingReward(RewardComponent):
    """Reward for solving puzzles quickly - +25 points"""

    async def score(self, state: Dict[str, Any], action: Dict[str, Any]) -> float:
        # Placeholder - would detect puzzle completion
        return 0.0


class MoveEffectivenessReward(RewardComponent):
    """Reward for consistently using type advantages - +8 points"""

    async def score(self, state: Dict[str, Any], action: Dict[str, Any]) -> float:
        # Placeholder - would track type effectiveness usage
        return 0.0


class EvolutionTimingReward(RewardComponent):
    """Reward for evolving Pokemon at optimal times - +15 points"""

    async def score(self, state: Dict[str, Any], action: Dict[str, Any]) -> float:
        # Placeholder - would analyze evolution timing
        return 0.0


class HMUsageReward(RewardComponent):
    """Reward for using HMs in appropriate situations - +10 points"""

    async def score(self, state: Dict[str, Any], action: Dict[str, Any]) -> float:
        # Placeholder - would detect appropriate HM usage
        return 0.0
