I have been working with OpenAI Gym for reinforcement learning projects and noticed something confusing about how rewards work. When I run the Breakout environment and the game ends (all lives lost), the environment returns done=True but still gives reward=0.
This seems strange to me. Wouldn’t it make more sense to give a negative reward when the agent fails? That way the agent would learn that losing is bad.
I also see that during regular gameplay, if no blocks get hit, the reward is zero too. How is the agent supposed to know the difference between a regular move that doesn’t hit anything and actually losing the game? Both give the same zero reward.
Is there a reason why Gym environments are designed this way? It makes it harder for agents to understand what actions are truly harmful versus just neutral.
yeah, that’s kinda the point - terminal states get zero reward to avoid messing up value estimates. if u add negative rewards when the game ends, you’re basically saying terminal states r bad, not the actions that got ya there. agents learn losing is bad by shorter episodes and lower total scores.
It’s actually about how value-based learning works. Q-learning and DQN agents learn to estimate expected rewards from each state-action pair. Terminal states naturally have zero future reward since there’s nothing left to do. Here’s the thing - adding negative rewards at termination double-penalizes the agent. The bad performance already shows up in the low cumulative score during the episode. Extra punishment creates a mismatch between the environment’s natural rewards and your artificial penalty. I’ve tried custom reward shaping in similar setups. Sure, it can speed up early learning, but it usually creates worse policies down the road. The agent ends up optimizing to avoid your fake penalty instead of maximizing what actually matters. OpenAI Gym keeps the original game mechanics exactly for this reason - to prevent these distortions and keep research results comparable across different implementations.
Gym environments match their reward structure to the game’s actual mechanics. In Breakout, you get rewards for hitting blocks, and zero reward means you did something neutral that didn’t advance the game. When an episode ends with ‘done=True,’ there’s no explicit negative reward on purpose - agents learn from cumulative scores over time. A shorter episode with fewer points already signals a worse outcome. If you want explicit negative rewards when episodes end, just create a custom wrapper around the environment.
totally get what u mean! gym’s setup focus on overall scores, not just single actions. when u lose, the lack of points kinda serves as a penalty. adding negative rewards might confuse the agent more. it’s all about learning over time.
The zero reward thing makes total sense from a data collection angle. I’ve hit this exact problem training agents at scale.
Here’s why - you don’t want to add penalties that weren’t in the original game. The agent learns from patterns across thousands of episodes, not individual steps. A game ending quickly with low total reward IS the punishment.
If you need custom reward shaping though, skip writing wrapper code from scratch. I automate all my RL experiment changes through Latenode workflows. You can set up automatic reward adjustments, episode logging, and A/B test different reward structures without touching your training code.
Last month I used it to modify rewards across 20 different Atari environments for a study. The workflow caught terminal states and applied custom penalties based on performance metrics. Way cleaner than juggling multiple wrapper classes.
Latenode handles environment modifications while your agent focuses on learning. Much more scalable.