Essays about: "CartPole"
Showing result 1 - 5 of 6 essays containing the word CartPole.
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1. Fine-tuning Bot Play Styles From Demonstration
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : 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. READ MORE
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2. Playing Atari Breakout Using Deep Reinforcement Learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE
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3. Generation and Detection of Adversarial Attacks for Reinforcement Learning Policies
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE
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4. Control of an Inverted Pendulum Using Reinforcement Learning Methods
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE
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5. Deep Reinforcement Learning in Cart Pole and Pong
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE
