Reward Function
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A Reward Function is a function that measures reward, quantifying the desirability of an action or outcome in various AI contexts.
- Context:
- It can (typically) quantify the immediate reward received by an agent after taking an action in a given state.
- It can (often) incorporate a variety of metrics, including monetary gains, points, or abstract values that represent success or failure in a given scenario.
- It can range from simple formulas like rewards for reaching a goal to complex structures that consider long-term gains and strategic considerations.
- It can be designed by a Reward Function Design Task.
- It can drive the behavior of learning algorithms by defining what is considered 'successful' or 'optimal' in a given environment.
- It can be manually designed by developers or automatically learned from data, adapting dynamically as the system interacts with its environment.
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- Example(s):
- Game-related Reward Functions:
- a Point-Based Reward Function used in video games, where players earn points for collecting items or defeating enemies.
- a Level Completion Reward Function in puzzle games, rewarding the agent for solving each level.
- a High Score Reward Function in arcade-style games, encouraging the agent to beat previous high scores.
- Financial Reward Functions:
- a Financial Reward Function in algorithmic trading systems, where actions are evaluated based on potential profit and loss.
- a Portfolio Optimization Reward Function in investment management, balancing risk and return to maximize portfolio value.
- Robotics and Control Reward Functions:
- a Distance-Based Reward Function in robot navigation, rewarding the agent for minimizing the distance to a target location.
- a Energy Efficiency Reward Function in smart home systems, encouraging the agent to optimize energy consumption.
- a Safety Reward Function in autonomous vehicles, prioritizing actions that minimize the risk of accidents.
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- Game-related Reward Functions:
- Counter-Example(s):
- Unsupervised Learning Tasks, where there is no explicit reward function guiding the learning process.
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- See: Learned Reward Function, Utility Function, Cost Function, Reinforcement Learning, Artificial Intelligence.