{"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/angrier-birds-bayesian-reinforcement-learning","title":"Angrier Birds: Bayesian reinforcement learning","arxiv_id":"1601.01297","date":"2016-01-06","proceeding":null,"authors":["Imanol Arrieta Ibarra","Bernardo Ramos","Lars Roemheld"],"abstract":"We train a reinforcement learner to play a simplified version of the game\nAngry Birds. The learner is provided with a game state in a manner similar to\nthe output that could be produced by computer vision algorithms. We improve on\nthe efficiency of regular {\\epsilon}-greedy Q-Learning with linear function\napproximation through more systematic exploration in Randomized Least Squares\nValue Iteration (RLSVI), an algorithm that samples its policy from a posterior\ndistribution on optimal policies. With larger state-action spaces, efficient\nexploration becomes increasingly important, as evidenced by the faster learning\nin RLSVI.","url_abs":"http://arxiv.org/abs/1601.01297v2","url_pdf":"http://arxiv.org/pdf/1601.01297v2.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":"angrier-birds-bayesian-reinforcement-learning","repo_url":"https://github.com/imanolarrieta/angrybirds","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"efficient-exploration","task_name":"Efficient Exploration"},{"task_slug":"q-learning","task_name":"Q-Learning"},{"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":[{"method_slug":"q-learning","method_name":"Q-Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}