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AI agents learn to play Super Mario Bros. with human-like behavior

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to train AI agents to play Super Mario Bros. more effectively and human-like. By training Proximal Policy Optimization (PPO) agents on a large and diverse set of procedurally generated levels, the agents demonstrated improved generalization to unseen levels. Further fine-tuning with Diffusion-Reward Adversarial Imitation Learning (DRAIL) using human gameplay data increased the agents' action-level human-likeness while retaining their core gameplay personas. AI

IMPACT This research demonstrates improved generalization and human-likeness in game-playing AI, potentially influencing agent training methodologies.

RANK_REASON The cluster describes a research paper detailing a novel method for training game-playing AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI agents learn to play Super Mario Bros. with human-like behavior

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The cluster describes a research paper detailing a novel method for training game-playing AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Stack more levels: how to get general and human-like Mario playing

    Super Mario Bros. is widely used in game AI and procedural content generation (PCG), yet it remains unclear whether a model-free agent can reliably win unseen levels. To address this question, we train Proximal Policy Optimization (PPO) agents on increasingly large sets of PCG-ge…

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