Why is RL More Preferred Than Evolution-Inspired Approaches?

Disclaimer: This is not an argument or a claim. The goal is to reflect on common patterns and offer a perspective rooted in current trends. I'm personally interested in both approaches and believe each has its place.

Reinforcement Learning (RL) and evolution-inspired algorithms (like genetic algorithms or evolution strategies) are both used to train agents. Both aim to optimize performance in environments by improving a policy or set of parameters over time. But in many modern applications, especially involving deep networks and high-dimensional inputs, RL is more commonly used.

Why is that?

Let's explore the main reasons.

1. Sample Efficiency Is a Major Factor

At the core of this preference lies sample efficiency.

In a typical RL setup, agents update their policies several times per episode using gradient-based learning. That means with every experience the agent collects, it can improve. Whether you're using on-policy methods like PPO or off-policy ones like DDPG or SAC, the learning is incremental and relatively fast.

Compare this with a naive evolutionary algorithm (like Random Search): you'd need to evaluate a full episode just to assess a single policy variation. Multiply that by the number of perturbations in a population, and you're burning through a lot of data before making even one meaningful update.

So the real question becomes: Do we have a fast and accurate simulator? If not, or worse, if we're working with a real-world system (like a physical robot), this kind of inefficiency can be a showstopper.

2. Evolution Is Harder to Scale Efficiently

Parallelization sounds like a win for evolution. In theory, you can evaluate all children in a generation at once. But in practice, this involves creating independent copies of your neural network, sending full weight matrices between threads or devices, and dealing with overhead in memory and communication.

It gets messy fast.

You could mitigate this by designing smarter systems (say, where each agent runs for several steps before syncing weights), but that adds engineering complexity. Even if everything's GPU-accelerated, weight copying isn't always negligible. It adds up especially when you scale up.

In contrast, Deep RL integrates tightly with modern deep learning frameworks, leveraging all the GPU optimization we've built for supervised learning. You're mostly just doing backprop, batch updates, and efficient matrix operations. Evolution doesn't benefit from this kind of hardware-level tuning, at least not yet.

3. Bigger Networks Break Evolution Faster

Deep Learning loves big networks. But evolutionary algorithms? Not so much.

Random perturbations in a small network might help you explore usefully. But once you scale up to tens of millions of parameters, you're practically brute-forcing the search space. At that size, the chances of a good mutation drop sharply unless your population size, and therefore your compute budget, skyrockets.

So, you're stuck. You either:

• Handcraft better features to shrink the network, which works against the idea of end-to-end learning,
• Or throw compute at the problem, which is rarely feasible outside well-funded labs.

4. RL Offers Better Temporal Credit Assignment

One of RL's biggest strengths is its ability to link actions to outcomes, especially in tasks that unfold over long time horizons. For instance, in a game, an RL agent can learn that "pressing jump at the right time avoids an obstacle." It can assign credit to that specific action because it's optimizing step by step, using gradient-based updates. In contrast, evolutionary methods treat the policy as a black box. They just see "this policy got a high score" without any understanding of why it worked.

This difference matters a lot in control problems with long-term dependencies or delayed rewards. RL is fundamentally designed for these scenarios, for problems involving sequential decision-making, where each action can influence future states and rewards. Techniques like the Bellman equation or advantage estimation allow it to trace rewards back to the decisions that caused them even if those decisions were taken many steps earlier. That makes RL much more effective in tasks like robot locomotion, where each movement affects long-term stability, or chess, where the payoff might not come until 50 moves later.

Evolutionary algorithms, on the other hand, can work well in short, reactive tasks, but they struggle when credit assignment becomes ambiguous. Without knowing which part of the behavior contributed to success, evolution can waste a lot of compute trying to rediscover what RL pinpoints more directly.

5. Gradient Descent Still Reigns in Optimization

It's worth pointing out that deep RL methods inherit their optimization machinery from supervised learning. That means they use tools like stochastic gradient descent (SGD), adaptive learning rates (Adam, RMSProp), and regularization.

This gives them a huge leg up in terms of convergence, fine-tuning, and scalability. Evolutionary algorithms, by contrast, optimize via perturbation and selection. It's like shooting arrows in the dark compared to adjusting your aim based on feedback.

In fact, evolution is often used when gradient information isn't available or meaningful. That's a very different scenario from the one RL typically tackles.

Where Evolution Still Shines

All this said, evolution-inspired methods absolutely have their place. They're often better in situations where:

• The reward function is ill-defined or non-differentiable
• Search space is discrete or structured (e.g., architecture search, rule evolution)
• Parallel computing is cheap and abundant
• You care more about robustness than convergence speed

One elegant example is in the World Models paper. The researchers trained a huge world model with RL, but used a simple linear actor trained via evolution. It was a smart hybrid approach that used each tool where it worked best.

Similarly, OpenAI showed that large-scale evolution strategies could rival Deep Q-Networks (DQN) on Atari games.¹ But they needed huge compute budgets to do it.

Different Tools for Different Problem Spaces

It's also helpful to recognize that RL and evolutionary algorithms don't always compete directly. In fact, they solve problems in different domains:

• Genetic algorithms search finite-dimensional parameter spaces. They're often framed like mathematical optimization: find vector x that maximizes function f(x).
• Reinforcement learning operates in the space of policies that map states to actions. It's rooted in dynamic programming, designed to solve control and decision-making problems.

So using RL to solve something better handled by a traditional optimizer or an EA might just be overkill. It's like using a rocket to deliver a letter across town.

Final Thoughts

So, why is RL often preferred today?

Because it's:

• More sample efficient
• Better optimized for hardware
• Scales better with large models
• Handles long-term dependencies
• Built on the foundation of supervised deep learning

But that doesn't mean evolution-inspired methods are obsolete. In fact, they may become more relevant as compute gets cheaper or as we look for more robust, less gradient-dependent algorithms.

Both paradigms have their strengths. And some of the best results come from combining them.

Take this with a grain of salt. These are just my thoughts.