Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 Games

• URL: http://arxiv.org/abs/2410.16653v1
• Date: Tue, 22 Oct 2024 02:57:44 GMT
• Title: Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 Games
• Authors: Kimiya Saadat, Richard Zhao,
• Abstract summary: This study examines the proposed idea in ten different Atari 2600 environments using the Atari 2600 RAM as the input state. We discuss the advantages of using transfer learning from a single-player training process over training in a two-player setting from scratch.
• Score: 1.03590082373586
• License:
• Abstract: Playing two-player games using reinforcement learning and self-play can be challenging due to the complexity of two-player environments and the possible instability in the training process. We propose that a reinforcement learning algorithm can train more efficiently and achieve improved performance in a two-player game if it leverages the knowledge from the single-player version of the same game. This study examines the proposed idea in ten different Atari 2600 environments using the Atari 2600 RAM as the input state. We discuss the advantages of using transfer learning from a single-player training process over training in a two-player setting from scratch, and demonstrate our results in a few measures such as training time and average total reward. We also discuss a method of calculating RAM complexity and its relationship to performance.

Related papers

• Personalized Dynamic Difficulty Adjustment -- Imitation Learning Meets Reinforcement Learning [44.99833362998488]
  In this work, we explore balancing game difficulty using machine learning-based agents to challenge players based on their current behavior. This is achieved by a combination of two agents, in which one learns to imitate the player, while the second is trained to beat the first. In our demo, we investigate the proposed framework for personalized dynamic difficulty adjustment of AI agents in the context of the fighting game AI competition.
  arXiv  Detail & Related papers  (2024-08-13T11:24:12Z)
• Reinforcement Learning for High-Level Strategic Control in Tower Defense Games [47.618236610219554]
  In strategy games, one of the most important aspects of game design is maintaining a sense of challenge for players. We propose an automated approach that combines traditional scripted methods with reinforcement learning. Results show that combining a learned approach, such as reinforcement learning, with a scripted AI produces a higher-performing and more robust agent than using only AI.
  arXiv  Detail & Related papers  (2024-06-12T08:06:31Z)
• Guarantees for Self-Play in Multiplayer Games via Polymatrix Decomposability [2.2636685010313364]
  Self-play is a technique for machine learning in multi-agent systems where a learning algorithm learns by interacting with copies of itself. We show that in two-player constant-sum games, self-play that reaches Nash equilibrium is guaranteed to produce strategies that perform well against any post-training opponent. For the first time, our results identify a structural property of multiplayer games that enable performance guarantees for the strategies produced by a broad class of self-play algorithms.
  arXiv  Detail & Related papers  (2023-10-17T18:33:21Z)
• Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games [58.720142291102135]
  We describe an effort to add an experimental reinforcement learning system to an existing automated game testing solution based on scripted bots. We show a use-case of leveraging reinforcement learning in game production and cover some of the largest time sinks anyone who wants to make the same journey for their game may encounter. We propose a few research directions that we believe will be valuable and necessary for making machine learning, and especially reinforcement learning, an effective tool in game production.
  arXiv  Detail & Related papers  (2023-07-19T18:19:23Z)
• Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals [69.76245723797368]
  Read and Reward speeds up RL algorithms on Atari games by reading manuals released by the Atari game developers. Various RL algorithms obtain significant improvement in performance and training speed when assisted by our design.
  arXiv  Detail & Related papers  (2023-02-09T05:47:03Z)
• Applying supervised and reinforcement learning methods to create neural-network-based agents for playing StarCraft II [0.0]
  We propose a neural network architecture for playing the full two-player match of StarCraft II trained with general-purpose supervised and reinforcement learning. Our implementation achieves a non-trivial performance when compared to the in-game scripted bots.
  arXiv  Detail & Related papers  (2021-09-26T20:08:10Z)
• Learning to Play Soccer From Scratch: Sample-Efficient Emergent Coordination through Curriculum-Learning and Competition [1.675857332621569]
  This work proposes a scheme that allows learning complex multi-agent behaviors in a sample efficient manner, applied to 2v2 soccer. The problem is formulated as a Markov game, and solved using deep reinforcement learning. Our results show that high quality soccer play can be obtained with our approach in just under 40M interactions.
  arXiv  Detail & Related papers  (2021-03-09T01:57:16Z)
• An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games [79.23847247132345]
  This work investigates how well an artificial agent can benefit from playing guessing games when later asked to perform on novel NLP downstream tasks such as Visual Question Answering (VQA) We propose two ways to exploit playing guessing games: 1) a supervised learning scenario in which the agent learns to mimic successful guessing games and 2) a novel way for an agent to play by itself, called Self-play via Iterated Experience Learning (SPIEL)
  arXiv  Detail & Related papers  (2021-01-31T10:30:48Z)
• Reinforcement Learning with Dual-Observation for General Video Game Playing [12.33685708449853]
  General Video Game AI Learning Competition aims to develop agents capable of learning to play different game levels unseen during training. This paper summarises the five years' General Video Game AI Learning Competition editions. We present a novel reinforcement learning technique with dual-observation for general video game playing.
  arXiv  Detail & Related papers  (2020-11-11T08:28:20Z)
• Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Game [71.24825724518847]
  We present a study on how popular reinforcement learning algorithms can be adapted to learn and to play a real-world implementation of a competitive multiplayer card game. We propose specific training and validation routines for the learning agents, in order to evaluate how the agents learn to be competitive and explain how they adapt to each others' playing style.
  arXiv  Detail & Related papers  (2020-04-08T14:11:05Z)
• Model-Based Reinforcement Learning for Atari [89.3039240303797]
  We show how video prediction models can enable agents to solve Atari games with fewer interactions than model-free methods. Our experiments evaluate SimPLe on a range of Atari games in low data regime of 100k interactions between the agent and the environment.
  arXiv  Detail & Related papers  (2019-03-01T15:40:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.