3 ms·
It was. Around the time this came out, something like half of the new ML papers were about reinforcement learning. The problem is that it’s incredibly slow and
by eigenvalue 3y ago
It was. Around the time this came out, something like half of the new ML papers were about reinforcement learning. The problem is that it’s incredibly slow and inefficient compared to any learning where you have access to a gradient and can use that to choose more targeted weight updates.
But there are certain applications where it’s the only good way of doing it (for example in games, where you don’t have access to a gradient over the space of how good a certain move is given the current game state, and it’s relatively quick and and efficient to simulate the evolution of the game).
- rnimmer 3y agoneuroevolution strategies are another approach to games, for what it's worth (since you said 'only').
- Jagerbizzle 3y agoFor those of us like me who are unfamiliar, can you recommend any useful reading on the topic?
- rnimmer 3y agoYes, take a look at Ken Stanley's web presence: NEAT: https://www.cs.ucf.edu/~kstanley/neat.html https://www.cs.ucf.edu/~kstanley/neat.html HyperNEAT: http://eplex.cs.ucf.edu/hyperNEATpage/HyperNEAT.html http://eplex.cs.ucf.edu/hyperNEATpage/HyperNEAT.html There are explanations, links to the research papers, and links to implementations. Here is a working example that runs in your browser using Javascript: https://liquidcarrot.io/example.flappy-bird/ https://liquidcarrot.io/example.flappy-bird/