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More specifically, the method takes\nadvantage of asynchronous advantage actor critic (A3C) architecture to\ngeneralize a target game using an agent trained on a source game in Atari.\nInstead of fine-tuning a pre-trained model for the target game, we propose a\nlearning approach to update the model using multiple agents trained in parallel\nwith different representations of the target game. Visual mapping between video\nsequences of transfer pairs is used to derive new representations of the target\ngame; training on these visual representations of the target game improves\nmodel updates in terms of performance, data efficiency and stability. In order\nto demonstrate the functionality of the architecture, Atari games Pong-v0 and\nBreakout-v0 are being used from the OpenAI gym environment; as the source and\ntarget environment.","url_abs":"http://arxiv.org/abs/1809.00397v1","url_pdf":"http://arxiv.org/pdf/1809.00397v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"visual-transfer-between-atari-games-using","repo_url":"https://github.com/sowmya-mp/rl_a3c_pytorch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"atari-games","task_name":"Atari Games"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"openai-gym","task_name":"OpenAI Gym"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.00397","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.00397"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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