In reinforcement learning (RL), reckless behavior typically occurs because penalty terms for undesirable actions are either omitted from the reward function or assigned absolute values that are too small to deter the agent.

University of Michigan - Ann Arbor

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Misconfigured reward functions can produce three abnormal behavior patterns in reinforcement learning: reckless, timid, and greedy behavior. A reckless agent ignores important negative side effects or constraints while maximizing reward. A timid agent becomes stagnant when penalties are too numerous or too large relative to the principal rewards. A greedy agent engages in unproductive iterations and neglects long-term reward, particularly under sparse reward conditions.

Types of Abnormal Agent Behavior from Misconfigured Reinforcement-Learning Rewards

It's due to too many penalty term with high absolute value compared to the principal rewards.

Common Reason of Timid Behavior

It always happens in RL with sparse reward conditions. It will be helpful to consider changing some tiny rewards to punishment penalty to force the agent to pursue the main reward.

Common Reason of Greedy Behavior

Common Reason of Reckless Behavior

Reckless behavior in reinforcement learning (RL) is an abnormal agent behavior where the agent ignores critical negative side effects or constraints in order to maximize its reward. This behavior often leads to meaningless or unsafe outcomes, particularly in multi-task RL environments.

Reckless Behavior in Reinforcement Learning

Timid behavior in reinforcement learning is an abnormal pattern in which an agent becomes stagnant because penalties are too numerous or too large relative to the principal rewards.

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