![]() ![]() It involves navigating a bird through a bunch of obstacles. Though, this problem can be solved using naive RL implementation, it requires good feature definitions to set up the problem. ![]() Our goal is to develop a CNN model to learn features from just snapshots of the game and train the agent to take the right actions at each game instance. 1 Reinforcement learning is essential when it is not sufficient to tackle problems programming the agent with just a few predetermined behaviors. It is a way to teach the agent to make the right decisions under uncertainty and with very high dimensional input (such as a camera) making it experiencing scenarios. In this way, the learning can happen online and the agent can learn to react to even the rarest of scenarios which the brutal programming would never consider. I NTRODUCTION P ROBLEM D EFINITION Flappy bird (Figure 1) is a game in which the player guides the bird, which is the of the game through the space between pairs of pipes. At each instant there are two actions that the player can take: to press the key, which makes the bird jump upward or not pressing any key, which makes it descend at a constant rate. Today, the recent advances in deep neural networks, in which several layers of nodes are used to build up progressively more abstract representations of the data, have made it possible for machine learning models to learn concepts such as object categories directly from raw sensory data. It is has also been observed that deep convolutional networks, which use hierarchical layers of tiled convolutional filters to mimic the effects of receptive fields produce promising results in solving computer vision problems such as classification and detection. ![]()
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