dudley students courses greenfoot project flappy bird

DUDLEY STUDENTS COURSES GREENFOOT PROJECT FLAPPY BIRD UPDATE

Instead, a reinforcement learning setting tries to evaluate the actions at a given state based on the reward it observes executing it. Unlike the common classification problem, we have beled data to train the model on. This is similar to a classification problem. The task of the AI agent when the model gets deployed is to extract images of game instances and output the necessary action to be taken from the set of feasible actions that can be taken. 3 M ETHODS In the following section, we describe how the model is parameterized and the algorithm. It is this simple learning framework and their stupendous results in playing the Atari games, inspired us to implement a similar algorithm for this project. The model learns playing with the game emulator and learns to make good decisions over time. Another advantage of this pipeline is the complete absence of labeled data.

DUDLEY STUDENTS COURSES GREENFOOT PROJECT FLAPPY BIRD UPDATE

They use a of previous experiences from which are randomly sampled to update the network so as to experiences and delayed updates for the cloned model from which target values are obtained (explained in detail later) to better the stability. They also discuss a few techniques to improve the efficiency of training and better the stability. The deep Neural Networks acts as the approximate function to represent the in Qlearning. are able to successfully train agents to play these games using reinforcement learning, surpassing human on multiple games Here, they have developed a novel agent, a deep (DQN) combining reinforcement learning with deep neural networks. previous venture was to learn and play the Atari 2600 games just from the raw pixel data. Their recent success, AlphaGo the Go agent that has been giving a stiff competition to the experts in the game show clearly the potential of what Deep learning is 1 Figure 2: Schematic Architecture of the Convolutional Neural Network. The goal here is to develop a deep neural network to learn game specific features just from the raw pixels and Figure 1: Flappybird Game Schematics 2 R ELATED W ORK Google efforts to use Deep learning techniques to play games have paved way to looking at artificial Intelligence problems with a completely different lens. 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. 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. 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. 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. 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. 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. 1 Reinforcement learning is essential when it is not sufficient to tackle problems programming the agent with just a few predetermined behaviors. 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. Though, this problem can be solved using naive RL implementation, it requires good feature definitions to set up the problem. It involves navigating a bird through a bunch of obstacles.

The game we are considering for this project is the popular mobile game Flappy Bird. The goal here is to develop a more general framework to learn game specific features and solve the problem.

Solving such problems using game search algorithms require careful domain specific feature definitions, making them averse to scalability. Learning to play games has been one among of the popular topics researched in AI today.

Inspired and we propose a reinforcement learning to learn and play this game.

Preview text Playing FlappyBird with Deep Reinforcement Learning Naveen Appiah Mechanical Engineering Sagar Vare Stanford ICME Abstract decide on what actions to take.

Summary - Coloring black and white world using deep neural nets.Practical - A few useful things to know about machine learning.Summary - Approximation by superpositions of a sigmoidal function.Practical - Automatic tumor segmentation from mri scans.

Dudley students courses greenfoot project flappy bird