osu!mania Reinforcement Learning Agent - PowerPoint PPT Presentation

Feb 13, 2023 •6 likes •136 views

osu!mania Reinforcement Learning Agent ichrysomallis@isc.tuc.gr 2014030078 Contents Introduction osu!mania game Graphical User Interface customization Agents environment Approach and

osu!mania Reinforcement Learning Agent Χρυσομάλλης Ιάσων ichrysomallis@isc.tuc.gr 2014030078
Contents  Introduction  osu!mania game  Graphical User Interface customization  Agent’s environment  Approach and variable definition  Q-learning  Deep reinforcement learning  Future plans 2
Introduction Topic: Develop an agent able to learn how to play the video game osu!mania, through reinforcement learning. Two agents :  Q-learning agent  Deep reinforcement learning agent 3
osu!mania game Rhythm game , notes are falling Single-tap notes 1. Hold notes 2. Judgment bar 3. Player keys 4. Combo 5. Hitburst 6. Overall accuracy 7. Score 8. 4
Graphical User Interface customization Fully customizable environment, all elements can be changed Each element is painted with solid color RGB = [X, 100, 100], where X is in accordance with the element’s identity (see numbers) 5
Agent’s environment Record screenshots and translate information based on the RGB values given Small fraction of the screen includes relevant information , specific boxes are being recorded 6
Approach and variable definition (1) Identical behavior on each column, problem can be narrowed down to single column learning  Agent’s actions : Instantaneous key tap 1. Key press (no release) 2. Key release 3. Do nothing 4. 7
Approach and variable definition ( 2 )  Rewards:  Epsilon: o Initial value = 1 o Decay value = 0.9977 o Minimum value = 0.01 8
Approach and variable definition ( 3 )  State:  One column of 200 pixels  Only red (R) layer  Three possible values (no note, singe-tap note, hold note) Deep reinforcement learning: o Raw input of the column Q-learning: o Only 8 pixels due to state complexity, taking one pixel every 15 pixels of the recorded column 9
Q-learning  Algorithm:  Steps:  Receive current state  Choose an action based on epsilon  Execute the action  Receive new state  Check if song is over  Update Q-table 10
Deep reinforcement learning  Neural network model (Keras):  Steps: Identical steps apart from last one. Save transitions in temporary memory and train the model with a smaller, randomly selected sample group (batch). 11
Results Q-learning agent DQN agent 12
Future plans  Try different combinations of neural network model layers  Design the neural network model in TensorFlow  Run the agent on GPU, instead of CPU  Make use of a high end computer 13

Recommend

Multi-agent learning Multi-agent reinforcement learning Gerard Vreeswijk , Intelligent Systems

Multi-agent learning Multi-agent reinforcement learning Multi-agent learning Multi-agent reinforcement learning Gerard Vreeswijk , Intelligent Systems Group, Computer Science Department, Faculty of Sciences, Utrecht University, The Netherlands.

752 views • 21 slides

Foundations of Machine Learning Reinforcement Learning Reinforcement Learning Agent exploring

Foundations of Machine Learning Reinforcement Learning Reinforcement Learning Agent exploring environment. Interactions with environment: action state Agent Environment reward Problem: find action policy that maximizes cumulative reward

828 views • 66 slides

RL Overview of topics About Reinforcement Learning The Reinforcement Learning Problem

Introduction to Reinforcement Learning RL Overview of topics About Reinforcement Learning The Reinforcement Learning Problem Inside an RL agent Temporal difference learning Many faces of Reinforcement Learning What is

552 views • 35 slides

REINFORCEMENT LEARNING IN MULTI-AGENT SYSTEMS MACHINE LEARNING MEETUP DR. ANA PELETEIRO

REINFORCEMENT LEARNING IN MULTI-AGENT SYSTEMS MACHINE LEARNING MEETUP DR. ANA PELETEIRO RAMALLO 29-08-2016 TABLE OF CONTENTS MULTI-AGENT SYSTEMS GAME THEORY REINFORCEMENT LEARNING MULTI-AGENT LEARNING 2 ZALANDO Our purpose: to Zalando

1.45k views • 20 slides

Imitation Learning Spring 2019, CMU 10-403 Katerina Fragkiadaki Reinforcement learning Agent

Carnegie Mellon School of Computer Science Deep Reinforcement Learning and Control Imitation Learning Spring 2019, CMU 10-403 Katerina Fragkiadaki Reinforcement learning Agent state reward action S t R t A t R t+ 1 Environment S t+ 1

1.07k views • 52 slides

QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement

QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning Kyunghwan Son , Daewoo Kim, Wan Ju Kang, David Hostallero, Yung Yi School of Electrical Engineering, KAIST Cooperative Multi-Agent Reinforcement

662 views • 8 slides

The Reinforcement Learning Problem Robert Platt Northeastern University Agent Action Agent

The Reinforcement Learning Problem Robert Platt Northeastern University Agent Action Agent World Observation Reward On a single time step, agent does the following: 1. observe some information 2. select an action to execute 3. take note

374 views • 11 slides

Reinforcement Learning You can think of supervised learning as the teacher providing answers

Reinforcement Learning You can think of supervised learning as the teacher providing answers (the class labels) Reinforcement learning: In reinforcement learning, the agent learns Markov Decision Processes based on a punishment/reward

89 views • 6 slides

1 The DFG Algorithm - The DFG Algorithm - Rationale The basic cycle Obviously, for each

4.3. Reinforcement learning for forming coalitions: the DFG algorithm Reinforcement Learning (I) Wei (1995) Watkins (1989) DFG: Dissolution and Formation of Groups Lets use our single agent definition: Then an agent Ag has in Dat for each

58 views • 3 slides

Model Based Reinforcement Learning Katerina Fragkiadaki Model Anything the agent can use to

Carnegie Mellon School of Computer Science Deep Reinforcement Learning and Control Model Based Reinforcement Learning Katerina Fragkiadaki Model Anything the agent can use to predict how the environment will respond to its actions,

826 views • 65 slides

Reinforcement Learning Robert Platt Northeastern University Some images and slides are used

Reinforcement Learning Robert Platt Northeastern University Some images and slides are used from: 1. CS188 UC Berkeley 2. RN, AIMA Conception of agent act Agent World sense RL conception of agent Agent takes actions a Agent World s,r

1.03k views • 39 slides

Reinforcement Learning, cont. Models an agent trying to learn what to do / how to act

Reinforcement Learning, cont. Models an agent trying to learn what to do / how to act in a complex environment. So far, we have seen passive learning We learned utility values for a fixed world with fixed transitions (no real

737 views • 29 slides

Reinforcement Learning Environments Fully-observable vs partially-observable Single agent

Reinforcement Learning Environments Fully-observable vs partially-observable Single agent vs multiple agents Deterministic vs stochastic Episodic vs sequential Static or dynamic Discrete or continuous What is reinforcement

684 views • 39 slides

Reinforcement Learning II George Konidaris gdk@cs.brown.edu Fall 2019 Reinforcement Learning

Reinforcement Learning II George Konidaris gdk@cs.brown.edu Fall 2019 Reinforcement Learning t r t R = max : S A t =0 MDPs Agent interacts with an environment At each time t: Receives sensor signal s t

717 views • 40 slides

1 Deep Reinforcement Learning Qianqian Li, Nayeon Koong, Langtian He What is deep reinforcement

1 Deep Reinforcement Learning Qianqian Li, Nayeon Koong, Langtian He What is deep reinforcement learning? Agent/Actor + Action + Environment + State + Reward How does reinforcement learning work?

793 views • 31 slides

Reinforcement Learning Course 9.54 Final review Agent learning to act in an unknown environment

Reinforcement Learning Course 9.54 Final review Agent learning to act in an unknown environment Reinforcement Learning Setup S t Background and setup The environment is initially unknown or partially known It is also stochastic, the

550 views • 31 slides

DeepMind Self-Learning Atari Agent Human - level control through deep reinforcement learning

DeepMind Self-Learning Atari Agent Human - level control through deep reinforcement learning Nature Vol 518, Feb 26, 2015 The Deep Mind of Demis Hassabis Backchannel / Medium.com interview with David Levy Advanced Topics:

989 views • 68 slides

ROMA: Multi-Agent Reinforcement Learning with Emerging Roles Tonghan Wang, Heng Dong, Victor

ROMA: Multi-Agent Reinforcement Learning with Emerging Roles Tonghan Wang, Heng Dong, Victor Lesser, Chongjie Zhang Tsinghua University, UMass Amherst Multi-Agent Systems Robot Football Game Multi-Agent Assembly One Major Challenge of

426 views • 30 slides

YunQi 2050 - DRL Session Communication in Multi-agent Reinforcement Learning Ying Wen

YunQi 2050 - DRL Session Communication in Multi-agent Reinforcement Learning Ying Wen Department of Computer Science, University College London MediaGamma Ltd. ying.wen@cs.ucl.ac.uk 30 May, 2018 Multi-agent in Real-World Human

402 views • 27 slides

1. Algorithms for Inverse Reinforcement Learning 2. Apprenticeship learning via Inverse

1. Algorithms for Inverse Reinforcement Learning 2. Apprenticeship learning via Inverse Reinforcement Learning Algorithms for Inverse Reinforcement Learning Andrew Ng and Stuart Russell Motivation Given: (1) measurements of an agent's

379 views • 34 slides

Agent-Based Modeling and Simulation Introduction to Reinforcement Learning Dr. Alejandro

Agent-Based Modeling and Simulation Introduction to Reinforcement Learning Dr. Alejandro Guerra-Hernndez Universidad Veracruzana Centro de Investigacin en Inteligencia Artificial Sebastin Camacho No. 5, Xalapa, Ver., Mxico 91000

700 views • 58 slides

COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage :

COMP 138: Reinforcement Learning Instructor : Jivko Sinapov Webpage : https://www.eecs.tufts.edu/~jsinapov/teaching/comp150_RL_Fall2020/ BE a reinforcement learner You, as a class, will act as the learning agent BE a reinforcement learner

717 views • 37 slides

Reinforcement Learning A (almost) quick (and very incomplete) introduction Slides from David

Reinforcement Learning A (almost) quick (and very incomplete) introduction Slides from David Silver, Dan Klein, Mausam, Dan Weld Reinforcement Learning At each time step t : Agent executes an action A t Environment emits a reward Rt

1.1k views • 71 slides

DJ-MC: A Reinforcement Learning Agent for Music Playlist Recommendation Elad Liebman Maytal

DJ-MC: A Reinforcement Learning Agent for Music Playlist Recommendation Elad Liebman Maytal Saar-Tsechansky Peter Stone University of Texas at Austin May 11, 2015 1 / 35 Background & Motivation Many Internet radio services (Pandora,

362 views • 35 slides