Sökning: "CartPole"

Visar resultat 1 - 5 av 7 uppsatser innehållade ordet CartPole.

1. 1. Fine-tuning Bot Play Styles From Demonstration

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Felicia Fredriksson; [2023]
   Nyckelord :;

   Sammanfattning : In recent years, Reinforcement Learning (RL) has successfully been used to train agents for games. Nonetheless, in the game industry there is still a necessity for bots not only to succeed in the environments but also to act human-like while playing the game. LÄS MER

3. 2. 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

4. 3. Generation and Detection of Adversarial Attacks for Reinforcement Learning Policies

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

   Författare :Axel Drotz; Markus Hector; [2021]
   Nyckelord :Deep Reinforcement Learning; Adversarial Attacks; Adversarial Attack Detection; Fast Gradient Sign Method; Deep Deterministic Policy Gradient; Deep Q-Learning; Likelihood Ratio Test; CUSUM;

   Sammanfattning : In this project we investigate the susceptibility ofreinforcement rearning (RL) algorithms to adversarial attacks.Adversarial attacks have been proven to be very effective atreducing performance of deep learning classifiers, and recently,have also been shown to reduce performance of RL agents. LÄS MER

5. 4. Control of an Inverted Pendulum Using Reinforcement Learning Methods

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

   Författare :Joel Kärn; [2021]
   Nyckelord :Reinforcement Learning; Q-learning; DQN; CartPole; Inverted Pendulum; OpenAI;

   Sammanfattning : In this paper the two reinforcement learning algorithmsQ-learning and deep Q-learning (DQN) are used tobalance an inverted pendulum. In order to compare the two, bothalgorithms are optimized to some extent, by evaluating differentvalues for some parameters of the algorithms. LÄS MER

7. 5. Deep Reinforcement Learning in Cart Pole and Pong

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

   Författare :Dennis Kuurne Uussilta; Viktor Olsson; [2020]
   Nyckelord :Artificial Intelligence; Machine Learning; Rein-forcement Learning; Deep Q-learning Network; CartPole; Pong;

   Sammanfattning : In this project, we aim to reproduce previous resultsachieved with Deep Reinforcement Learning. We present theMarkov Decision Process model as well as the algorithms Q-learning and Deep Q-learning Network (DQN). We implement aDQN agent, first in an environment called CartPole, and later inthe game Pong. LÄS MER