{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-tamer-interactive-agent-shaping-in-high","title":"Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces","arxiv_id":"1709.10163","date":"2017-09-28","proceeding":null,"authors":["Garrett Warnell","Nicholas Waytowich","Vernon Lawhern","Peter Stone"],"abstract":"While recent advances in deep reinforcement learning have allowed autonomous\nlearning agents to succeed at a variety of complex tasks, existing algorithms\ngenerally require a lot of training data. One way to increase the speed at\nwhich agents are able to learn to perform tasks is by leveraging the input of\nhuman trainers. Although such input can take many forms, real-time,\nscalar-valued feedback is especially useful in situations where it proves\ndifficult or impossible for humans to provide expert demonstrations. Previous\napproaches have shown the usefulness of human input provided in this fashion\n(e.g., the TAMER framework), but they have thus far not considered\nhigh-dimensional state spaces or employed the use of deep learning. In this\npaper, we do both: we propose Deep TAMER, an extension of the TAMER framework\nthat leverages the representational power of deep neural networks in order to\nlearn complex tasks in just a short amount of time with a human trainer. We\ndemonstrate Deep TAMER's success by using it and just 15 minutes of\nhuman-provided feedback to train an agent that performs better than humans on\nthe Atari game of Bowling - a task that has proven difficult for even\nstate-of-the-art reinforcement learning methods.","url_abs":"http://arxiv.org/abs/1709.10163v2","url_pdf":"http://arxiv.org/pdf/1709.10163v2.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":"deep-tamer-interactive-agent-shaping-in-high","repo_url":"https://github.com/JulienDesvergnes/human-reinforcement-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"deep-tamer-interactive-agent-shaping-in-high","repo_url":"https://github.com/bharadwaj1098/Tamer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.10163","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}