Normally, when you develop a neural network, train it for object recognition (on normal objects like bike, car, plane, dog, cloud, etc.), and it turns out to perform very well, you would like to fine-tune it for e.g. recognizing dog breeds, and this is called fine-tuning.
On the other hand, in reinforcement learning, let's consider a game with rewards on checkpoints 1,2,3,…n (like game 2048, or any platformer like Mario). When you have a bot that plays and learns using some (e.g. value) neural net, it develops some style of solving the problem to reach some checkpoint k, and, after that, to reach the k+1 checkpoint, it probably will have to revalue the whole strategy.
In this situation, will the bot fine-tune itself? Does it makes sense to keep a replay buffer as is and to "reset" the neural net to train it from scratch, or it's better to stay with fine-tune approach?
On the other hand, in reinforcement learning, let's consider a game with rewards on checkpoints 1,2,3,…n (like game 2048, or any platformer like Mario). When you have a bot that plays and learns using some (e.g. value) neural net, it develops some style of solving the problem to reach some checkpoint k, and, after that, to reach the k+1 checkpoint, it probably will have to revalue the whole strategy.
In this situation, will the bot fine-tune itself? Does it makes sense to keep a replay buffer as is and to "reset" the neural net to train it from scratch, or it's better to stay with fine-tune approach?
