Are evolution and reinforcement learning related?
- Evolution and reinforcement learning are related in that they both involve a process of learning and adaptation over time.
- Evolution is a biological process that occurs over generations, where traits that enhance an organism's survival and reproduction are passed down to future generations. Evolution is driven by natural selection, favouring traits that provide an advantage in the struggle for survival.
- Reinforcement learning, on the other hand, is a machine learning type involving an agent learning from its environment through trial and error. The agent receives feedback in the form of rewards or punishments for its actions, and over time, it learns to choose activities that maximize its reward.
- Both evolution and reinforcement learning involve a selection process, where certain traits or actions are favoured over others based on their performance in the environment. In evolution, the process of selection occurs over generations, while in reinforcement learning, it occurs within the lifespan of the agent.
- Furthermore, both evolution and reinforcement learning can lead to the emergence of complex and adaptive behaviour. In evolution, complex behaviours and adaptations emerge through the gradual accumulation of small changes over many generations. In reinforcement learning, complex behaviours emerge through the trial-and-error process of learning.
Since we can see the similarities, what is their origin? (When you see similar cookies, it means a mould exists somewhere.)
In evolution, an organism evolves, but in RL, a strategy (Policy). So are we not more than a strategy, an idea?
It's incorrect to think of evolution as a 'reward/punishment' system. In fact, it's incorrect to think of evolution in individual terms at all. Evolution (as it's currently understood) is ablative. Genes are introduced into a species' genome by random processes, and then a slew of creatures from that species pour out into the world. If those with that gene survive and reproduce in sufficient numbers, the gene is retained; if not, not.
Evolution is like throwing spaghetti at the wall to see what sticks, over and over, reusing the ones that stick and idly adding new pastas over time. Biology doesn't 'learn'. Biology copes.
Reward/punishment systems are an evolutionary adaptation that often (but not necessarily) gives individual members of a species a greater chance of surviving and passing on their genes. It only applies to Animalia with sufficient nervous systems to seek out prey and avoid predation. Plants, fungi, filter-feeders, etc don't seem to have an equivalent, and yet they are still products of evolution.
We'll get different answers to the question "Are evolution and reinforcement learning related?" depending on how broadly we interpret it. Sure, there's a correlation here, but the correlation seems to be more by metaphor or analogy than by anything concrete and practical. In the real world these principles operate on different scales: the first on the level of the genome, the second on the level of an individual expression of the genome. The capacity for learning certainly has an impact on evolution within a given species, but from the evolutionary prospecting the capacity for learning is just a different kind of pasta tossed at the wall.
We can say that both reward/punishment and evolution increase or decrease the prevalence of certain patterns. In reinforcement learning, reward increases ("reinforces") the prevalence of the pattern of behavior that led to the reward. In evolution, successful reproduction increases the prevalence of the genetic patterns that led to the successful reproduction. They are equivalent in that way, and that is the key feature of both.
Note that genetic algorithms can be used for reinforcement learning. They are not mutually exclusive. We can think of genetic algorithms, and evolution, as specific examples of reinforcement learning. Specific examples of the principle that "if, when a pattern leads to X, you increase its prevalence, then you will end up with a greater prevalence of patterns that lead to X."
Also, whether the prevalence of the pattern actually increases depends on the amount of reward compared to a background level. Generally the pattern has to lead to more reward than competing patterns, in order to increase in prevalence. Or in an evolutionary context, the genetic pattern has to lead to more offspring than competing genetic patterns.
This principle is easily seen in policy-gradient methods within RL. In these methods, you let an agent perform a series of actions with some random variations, leading to some amount of reward. Then you adjust the agent's parameters to make it either more likely or less likely to perform those actions. The more reward the actions led to, the more you adjust the parameters to make the actions more likely.
The principle is still present in other RL algorithms, just with a little indirection. For instance, in algorithms based on value iteration, the agent learns to predict the reward resulting from different actions. The agent is more likely to select actions that it predicts will produce greater reward. This means that if the agent receives a large reward, it will more frequently perform the actions that led to that reward.
Evolution is a biological process that occurs over generations, where traits that enhance an organism's survival and reproduction are passed down to future generations. Evolution is driven by natural selection, favouring traits that provide an advantage in the struggle for survival.
Not really. That makes it sound far too much like a planned process and implies a continuity and connection between agents of different generations. But it's really just "who's alive is alive".
Like if you start with 1000 people then after 200 years all of them are dead. None of them was better or worse suited to live and regardless of their features all of them are dead. Now if some of them engaged in cloning and starting the clock on that clone anew, then 200 years from now still all the original 1000 people are dead but those clones are still around. And because they are perfect clones they will proceed in the same way so maybe it's not even the original clones that are around but the clones of the clones.
So if you're only interested in the question of the features of the people who are or will be still around, then this process would be "static" or at least "periodic" because you run through the same cycles over an over again. But for the individual in it, each cycle will be the only cycle they'll ever see and it's far from static or periodic, but full of unique experiences.
And the same applies if you don't produce a perfect clone but a slightly imperfect clone or in terms of humans a mixture of 2 people. They will also just live their life and die of natural (or unnatural) causes and if they reproduce their clones will be alive later on and if they didn't then other's or no one will be around. But for the individual agent there's no difference between that, because by that time they won't be around anyway. So purely in terms of evolution, there is no improvement or enhancement happening for the agent and they don't pass on genes that are beneficial for survival, they only pass on their own genes and if they had been able to survive for long enough to do so, then that might have been genes well suited for survival... Or they might just have been lucky. Like picture a school of fish and a predator just randomly eating a few. Might have been the fastest, brightest, most athletic or the opposite and either way it doesn't really matter. Seriously it could even go backwards. Like the offspring of a fighter might be less strong, but still survive due to their parents protecting them and so on or how inbreeding nobility didn't create "aristocrats" (the best), but more like the opposite of that.
So in terms of biological evolution it's not even trial and error and you don't so much learn from mistakes, it's rather that "mistakes" or bad luck, just remove you from the gene pool.
On the other hand learning by trial and error IS an improvement of the individual. The concept is basically that you encounter the same or similar situations often and rank your experience with them. So what was good is done again what is bad is avoided, mixed might be tried again, might be not. And as those who like things that get them killed are more likely to get themselves killed, while those who like things that let them survive long enough to pass on their genes are more likely to do so, there might be a connection between evolution and learning by trial and error. So it might have been necessary at some point to match patterns, store and identify them, and thus realize that we're running in circles, then to take the paths in them that felt the most beneficial and have beneficial be "living long enough to pass on their genes".
Now with regards to machine learning we're basically looking at the same problem from the other end. Meaning we have identified that the environment fits individuals according to it's needs. Not really but that's what it looks like and for our intents and purposes that is all that matters. So instead of being the agent and living your life. You create an environment that matches a problem of yours in order to make it fit individuals according to it's needs, that ultimately help you solve the problem.
So the first iteration would be a genetic algorithm, so you really just throw everything at it and see what sticks. The one(s) that stick the best are copied and modified a little less and the process starts anew. More often than not people literally call each of these training cycles a "generation".
Though if you do that on the computer than the difference between a genetic algorithm and a reinforcement learning system really aren't that large because whether one iteration is a training cycle or whether at the end of an iteration you kill all those performing bad and clone the rest, the result is pretty much the same. And as the set of parameters that you've updated to perform better on the challenge is a continuous object or a newly generated is completely irrelevant for the computer who treats that as the same thing.
If you do 1000 runs with a single agent or 1 run with 1000 agents that are slightly modified doesn't really matter. If you have the processing power you likely make large number of runs with large number of agents anyway.
It might be a little conceptually different as one has the ranking of the situation be a property of the environment, while the other has it as a property of the agent, but depending on the implementation that might be similarly irrelevant like whether it's a persistent or new set of parameters.