"""
Adaptive & Learning Reward Components

Rewards for improvement over time and meta-learning.
"""

from typing import Any, Dict

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


class MistakeRecoveryReward(RewardComponent):
    """Reward for correcting previous errors - +10 points"""

    async def score(self, state: Dict[str, Any], action: Dict[str, Any]) -> float:
        # Placeholder - would need mistake tracking and recovery detection
        return 0.0


class StrategyAdaptationReward(RewardComponent):
    """Reward for changing tactics based on type matchups - +15 points"""

    async def score(self, state: Dict[str, Any], action: Dict[str, Any]) -> float:
        # Placeholder - would need strategy analysis
        return 0.0


class ResourceConservationReward(RewardComponent):
    """Reward for efficient PP/item usage - +8 points"""

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


class PatternRecognitionReward(RewardComponent):
    """Reward for recognizing and adapting to trainer patterns - +12 points"""

    async def score(self, state: Dict[str, Any], action: Dict[str, Any]) -> float:
        # Placeholder - would need pattern analysis
        return 0.0


class RouteOptimizationReward(RewardComponent):
    """Reward for finding better paths on repeat visits - +20 points"""

    async def score(self, state: Dict[str, Any], action: Dict[str, Any]) -> float:
        # Placeholder - would need route comparison
        return 0.0


class BattlePreparationReward(RewardComponent):
    """Reward for healing/preparing before major battles - +15 points"""

    async def score(self, state: Dict[str, Any], action: Dict[str, Any]) -> float:
        # Placeholder - would need preparation detection
        return 0.0
