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.

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It's very important to know some common cases of abnormal behaviors due to improper reward setting. There are three types of them: rackless behavior, timid behavior and greedy behavior.  Reackless behavior will cause the agent ignoring some given serious side affect and leading to meaningless results especially in multi-task RL conditions. Timid Behavior will cause the agent stagnant. And greedy behavior will cause serious meaningless iterations and make the agent ignoring long-term reward. 

Abnormal Behavior Types Due to Improper Reward Setting

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.

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