Sökning: "Atari 2600"

Visar resultat 1 - 5 av 7 uppsatser innehållade orden Atari 2600.

1. 1. Playing Atari Breakout Using Deep Reinforcement Learning

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Jonas Nils Martin Lidman; Simon Jonsson; [2022]
   Nyckelord :Reinforcement learning; CartPole; Breakout; DQN;

   Sammanfattning : This report investigates the implementation of a Deep Reinforcement Learning (DRL) algorithm for complex tasks. The complex task chosen was the classic game Breakout, first introduced on the Atari 2600 console.The selected DRL algorithm was Deep Q-Network(DQN) since it is one of the first and most fundamental DRL algorithms. LÄS MER

3. 2. Application of Deep Q-learning for Vision Control on Atari Environments

   Master-uppsats, Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisation

   Författare :Jim Öhman; [2021]
   Nyckelord :Reinforcement learning; Atari 2600; Deep Q-learning; Myopic Agents; Vision Control; Physics and Astronomy;

   Sammanfattning : The success of Reinforcement Learning (RL) has mostly been in artificial domains, with only some successful real-world applications. One of the reasons being that most real-world domains fail to satisfy a set of assumptions of RL theory. LÄS MER

4. 3. A scalable species-based genetic algorithm for reinforcement learning

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Anirudh Seth; [2021]
   Nyckelord :neuroevolution; model encoding; distributed speciation; reinforcement learning; genetic algorithms; evolutionary computing; neuroevolution; model encoding; förstärkningsinlärning; genetiska algoritmer; evolutionär databehandling;

   Sammanfattning : Existing methods in Reinforcement Learning (RL) that rely on gradient estimates suffer from the slow rate of convergence, poor sample efficiency, and computationally expensive training, especially when dealing with complex real-world problems with a sizable dimensionality of the state and action space. In this work, we attempt to leverage the benefits of evolutionary computation as a competitive, scalable, and gradient-free alternative to training deep neural networks for RL-specific problems. LÄS MER

5. 4. Unsupervised state representation pretraining in Reinforcement Learning applied to Atari games

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Francesco Nuzzo; [2020]
   Nyckelord :;

   Sammanfattning : State representation learning aims to extract useful features from the observations received by a Reinforcement Learning agent interacting with an environment. These features allow the agent to take advantage of the low-dimensional and informative representation to improve the efficiency in solving tasks. LÄS MER

7. 5. DQN Tackling the Game of Candy Crush Friends Saga : A Reinforcement Learning Approach

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Alice Karnsund; [2019]
   Nyckelord :;

   Sammanfattning : This degree project presents a reinforcement learning (RL) approach called deep Q-network (DQN) for learning how to play the game Candy Crush Friends Saga (CCFS). The DQN algorithm is implemented together with three extensions, which in 2015 resulted in a new state-of-the-art on the Atari 2600 domain. LÄS MER