{"url":"/sota/atari-games-on-atari-2600-seaquest","task":{"name":"Atari Games","url":"/task/atari-games","note":null},"dataset":{"name":"Atari 2600 Seaquest","url":"/dataset/arcade-learning-environment"},"category":"Playing Games","categories":["Playing Games"],"category_note":null,"description":"The Atari 2600 Games task (and dataset) involves training an agent to achieve high game scores.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Playing Atari with Deep Reinforcement Learning](https://arxiv.org/pdf/1312.5602v1.pdf) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Score","Return"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Score":"higher","Return":null}},"counts":{"rows":57,"rows_with_code":50,"rows_with_paper_page":56,"rows_dated":56,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"GDI-H3(200M frames)","metrics":{"Score":"1000000"},"uses_additional_data":false,"paper_date":"2022-06-07","paper":"/paper/generalized-data-distribution-iteration","paper_url":"https://arxiv.org/abs/2206.03192v4","paper_title":"Generalized Data Distribution Iteration","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":2,"model":"GDI-H3","metrics":{"Score":"1000000"},"uses_additional_data":false,"paper_date":"2022-06-07","paper":"/paper/generalized-data-distribution-iteration","paper_url":"https://arxiv.org/abs/2206.03192v4","paper_title":"Generalized Data Distribution Iteration","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":3,"model":"Agent57","metrics":{"Score":"999997.63"},"uses_additional_data":false,"paper_date":"2020-03-30","paper":"/paper/agent57-outperforming-the-atari-human","paper_url":"https://arxiv.org/abs/2003.13350v1","paper_title":"Agent57: Outperforming the Atari Human Benchmark","code":"https://github.com/michaelnny/deep_rl_zoo","n_code_links":5,"syntology":{"n_ran":8,"n_unverified":1,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"R2D2","metrics":{"Score":"999996.7"},"uses_additional_data":false,"paper_date":"2019-05-01","paper":"/paper/recurrent-experience-replay-in-distributed","paper_url":"https://openreview.net/forum?id=r1lyTjAqYX","paper_title":"Recurrent Experience Replay in Distributed Reinforcement Learning","code":"https://github.com/opendilab/DI-engine/blob/main/ding/policy/r2d2.py","n_code_links":3,"syntology":null},{"rank_in_archive_order":5,"model":"MuZero","metrics":{"Score":"999976.52"},"uses_additional_data":false,"paper_date":"2019-11-19","paper":"/paper/mastering-atari-go-chess-and-shogi-by","paper_url":"https://arxiv.org/abs/1911.08265v2","paper_title":"Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model","code":"https://github.com/werner-duvaud/muzero-general","n_code_links":18,"syntology":{"n_ran":43,"n_unverified":21,"n_samples":64,"n_pointer_only_licence":62}},{"rank_in_archive_order":6,"model":"MuZero (Res2 Adam)","metrics":{"Score":"999659.18"},"uses_additional_data":false,"paper_date":"2021-04-13","paper":"/paper/online-and-offline-reinforcement-learning-by","paper_url":"https://arxiv.org/abs/2104.06294v1","paper_title":"Online and Offline Reinforcement Learning by Planning with a Learned Model","code":"https://github.com/DHDev0/Muzero-unplugged","n_code_links":2,"syntology":null},{"rank_in_archive_order":7,"model":"GDI-I3","metrics":{"Score":"943910"},"uses_additional_data":false,"paper_date":"2022-06-07","paper":"/paper/generalized-data-distribution-iteration","paper_url":"https://arxiv.org/abs/2206.03192v4","paper_title":"Generalized Data Distribution Iteration","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":8,"model":"GDI-I3","metrics":{"Score":"943910"},"uses_additional_data":false,"paper_date":"2021-06-11","paper":"/paper/gdi-rethinking-what-makes-reinforcement","paper_url":"https://arxiv.org/abs/2106.06232v6","paper_title":"GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":9,"model":"Ape-X","metrics":{"Score":"392952.3"},"uses_additional_data":false,"paper_date":"2018-03-02","paper":"/paper/distributed-prioritized-experience-replay","paper_url":"http://arxiv.org/abs/1803.00933v1","paper_title":"Distributed Prioritized Experience Replay","code":"https://github.com/ray-project/ray/tree/master/rllib","n_code_links":15,"syntology":{"n_ran":0,"n_unverified":15,"n_samples":15,"n_pointer_only_licence":0}},{"rank_in_archive_order":10,"model":"C51 noop","metrics":{"Score":"266434.0"},"uses_additional_data":false,"paper_date":"2017-07-21","paper":"/paper/a-distributional-perspective-on-reinforcement","paper_url":"http://arxiv.org/abs/1707.06887v1","paper_title":"A Distributional Perspective on Reinforcement Learning","code":"https://github.com/facebookresearch/Horizon","n_code_links":22,"syntology":{"n_ran":0,"n_unverified":1,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":11,"model":"Duel noop","metrics":{"Score":"50254.2"},"uses_additional_data":false,"paper_date":"2015-11-20","paper":"/paper/dueling-network-architectures-for-deep","paper_url":"http://arxiv.org/abs/1511.06581v3","paper_title":"Dueling Network Architectures for Deep Reinforcement Learning","code":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","n_code_links":73,"syntology":{"n_ran":5,"n_unverified":6,"n_samples":11,"n_pointer_only_licence":6}},{"rank_in_archive_order":12,"model":"Duel hs","metrics":{"Score":"37361.6"},"uses_additional_data":false,"paper_date":"2015-11-20","paper":"/paper/dueling-network-architectures-for-deep","paper_url":"http://arxiv.org/abs/1511.06581v3","paper_title":"Dueling Network Architectures for Deep Reinforcement Learning","code":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","n_code_links":73,"syntology":{"n_ran":5,"n_unverified":6,"n_samples":11,"n_pointer_only_licence":6}},{"rank_in_archive_order":13,"model":"IQN","metrics":{"Score":"30140"},"uses_additional_data":false,"paper_date":"2018-06-14","paper":"/paper/implicit-quantile-networks-for-distributional","paper_url":"http://arxiv.org/abs/1806.06923v1","paper_title":"Implicit Quantile Networks for Distributional Reinforcement Learning","code":"https://github.com/opendilab/DI-engine/blob/main/ding/policy/iqn.py","n_code_links":19,"syntology":{"n_ran":1,"n_unverified":1,"n_samples":2,"n_pointer_only_licence":0}},{"rank_in_archive_order":14,"model":"ASL DDQN","metrics":{"Score":"29278.6"},"uses_additional_data":false,"paper_date":"2023-05-07","paper":"/paper/train-a-real-world-local-path-planner-in-one","paper_url":"https://arxiv.org/abs/2305.04180v3","paper_title":"Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity","code":"https://github.com/xinjinghao/color","n_code_links":1,"syntology":null},{"rank_in_archive_order":15,"model":"Prior noop","metrics":{"Score":"26357.8"},"uses_additional_data":false,"paper_date":"2015-11-18","paper":"/paper/prioritized-experience-replay","paper_url":"http://arxiv.org/abs/1511.05952v4","paper_title":"Prioritized Experience Replay","code":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","n_code_links":77,"syntology":{"n_ran":78,"n_unverified":33,"n_samples":111,"n_pointer_only_licence":43}},{"rank_in_archive_order":16,"model":"Prior hs","metrics":{"Score":"25463.7"},"uses_additional_data":false,"paper_date":"2015-11-18","paper":"/paper/prioritized-experience-replay","paper_url":"http://arxiv.org/abs/1511.05952v4","paper_title":"Prioritized Experience Replay","code":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","n_code_links":77,"syntology":{"n_ran":78,"n_unverified":33,"n_samples":111,"n_pointer_only_licence":43}},{"rank_in_archive_order":17,"model":"NoisyNet-Dueling","metrics":{"Score":"16754"},"uses_additional_data":false,"paper_date":"2017-06-30","paper":"/paper/noisy-networks-for-exploration","paper_url":"https://arxiv.org/abs/1706.10295v3","paper_title":"Noisy Networks for Exploration","code":"https://github.com/opendilab/DI-engine","n_code_links":15,"syntology":{"n_ran":1,"n_unverified":2,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":18,"model":"DDQN (tuned) noop","metrics":{"Score":"16452.7"},"uses_additional_data":false,"paper_date":"2015-11-20","paper":"/paper/dueling-network-architectures-for-deep","paper_url":"http://arxiv.org/abs/1511.06581v3","paper_title":"Dueling Network Architectures for Deep Reinforcement Learning","code":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","n_code_links":73,"syntology":{"n_ran":5,"n_unverified":6,"n_samples":11,"n_pointer_only_licence":6}},{"rank_in_archive_order":19,"model":"DDQN (tuned) hs","metrics":{"Score":"14498.0"},"uses_additional_data":false,"paper_date":"2015-09-22","paper":"/paper/deep-reinforcement-learning-with-double-q","paper_url":"http://arxiv.org/abs/1509.06461v3","paper_title":"Deep Reinforcement Learning with Double Q-learning","code":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","n_code_links":97,"syntology":{"n_ran":56,"n_unverified":50,"n_samples":106,"n_pointer_only_licence":57}},{"rank_in_archive_order":20,"model":"Persistent AL","metrics":{"Score":"13230.74"},"uses_additional_data":false,"paper_date":"2015-12-15","paper":"/paper/increasing-the-action-gap-new-operators-for","paper_url":"http://arxiv.org/abs/1512.04860v1","paper_title":"Increasing the Action Gap: New Operators for Reinforcement Learning","code":"https://github.com/janhuenermann/neurojs","n_code_links":2,"syntology":null},{"rank_in_archive_order":21,"model":"DDQN+Pop-Art noop","metrics":{"Score":"10932.3"},"uses_additional_data":false,"paper_date":"2016-02-24","paper":"/paper/learning-values-across-many-orders-of","paper_url":"http://arxiv.org/abs/1602.07714v2","paper_title":"Learning values across many orders of magnitude","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":22,"model":"Gorila","metrics":{"Score":"10145.9"},"uses_additional_data":false,"paper_date":"2015-07-15","paper":"/paper/massively-parallel-methods-for-deep","paper_url":"http://arxiv.org/abs/1507.04296v2","paper_title":"Massively Parallel Methods for Deep Reinforcement Learning","code":"https://github.com/nandomp/AICollaboratory","n_code_links":3,"syntology":null},{"rank_in_archive_order":23,"model":"Bootstrapped DQN","metrics":{"Score":"9083.1"},"uses_additional_data":false,"paper_date":"2016-02-15","paper":"/paper/deep-exploration-via-bootstrapped-dqn","paper_url":"http://arxiv.org/abs/1602.04621v3","paper_title":"Deep Exploration via Bootstrapped DQN","code":"https://github.com/tensorflow/models","n_code_links":6,"syntology":{"n_ran":1,"n_unverified":1,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":24,"model":"Advantage Learning","metrics":{"Score":"8670.5"},"uses_additional_data":false,"paper_date":"2015-12-15","paper":"/paper/increasing-the-action-gap-new-operators-for","paper_url":"http://arxiv.org/abs/1512.04860v1","paper_title":"Increasing the Action Gap: New Operators for Reinforcement Learning","code":"https://github.com/janhuenermann/neurojs","n_code_links":2,"syntology":null},{"rank_in_archive_order":25,"model":"QR-DQN-1","metrics":{"Score":"8268"},"uses_additional_data":false,"paper_date":"2017-10-27","paper":"/paper/distributional-reinforcement-learning-with-1","paper_url":"http://arxiv.org/abs/1710.10044v1","paper_title":"Distributional Reinforcement Learning with Quantile Regression","code":"https://github.com/DLR-RM/stable-baselines3","n_code_links":17,"syntology":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":1}},{"rank_in_archive_order":26,"model":"DreamerV2","metrics":{"Score":"7480"},"uses_additional_data":false,"paper_date":"2020-10-05","paper":"/paper/mastering-atari-with-discrete-world-models-1","paper_url":"https://arxiv.org/abs/2010.02193v4","paper_title":"Mastering Atari with Discrete World Models","code":"https://github.com/opendilab/DI-engine","n_code_links":9,"syntology":{"n_ran":9,"n_unverified":6,"n_samples":15,"n_pointer_only_licence":0}},{"rank_in_archive_order":27,"model":"Recurrent Rational DQN Average","metrics":{"Score":"7460"},"uses_additional_data":false,"paper_date":"2021-02-18","paper":"/paper/recurrent-rational-networks","paper_url":"https://arxiv.org/abs/2102.09407v5","paper_title":"Adaptive Rational Activations to Boost Deep Reinforcement Learning","code":"https://github.com/ml-research/rational_activations","n_code_links":4,"syntology":{"n_ran":6,"n_unverified":0,"n_samples":6,"n_pointer_only_licence":3}},{"rank_in_archive_order":28,"model":"DARQN soft","metrics":{"Score":"7263"},"uses_additional_data":false,"paper_date":"2015-12-05","paper":"/paper/deep-attention-recurrent-q-network","paper_url":"http://arxiv.org/abs/1512.01693v1","paper_title":"Deep Attention Recurrent Q-Network","code":"https://github.com/5vision/DARQN","n_code_links":3,"syntology":null},{"rank_in_archive_order":29,"model":"Rational DQN Average","metrics":{"Score":"6603"},"uses_additional_data":false,"paper_date":"2021-02-18","paper":"/paper/recurrent-rational-networks","paper_url":"https://arxiv.org/abs/2102.09407v5","paper_title":"Adaptive Rational Activations to Boost Deep Reinforcement Learning","code":"https://github.com/ml-research/rational_activations","n_code_links":4,"syntology":{"n_ran":6,"n_unverified":0,"n_samples":6,"n_pointer_only_licence":3}},{"rank_in_archive_order":30,"model":"DQN noop","metrics":{"Score":"5860.6"},"uses_additional_data":false,"paper_date":"2015-09-22","paper":"/paper/deep-reinforcement-learning-with-double-q","paper_url":"http://arxiv.org/abs/1509.06461v3","paper_title":"Deep Reinforcement Learning with Double Q-learning","code":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","n_code_links":97,"syntology":{"n_ran":56,"n_unverified":50,"n_samples":106,"n_pointer_only_licence":57}},{"rank_in_archive_order":31,"model":"VPN","metrics":{"Score":"5628"},"uses_additional_data":false,"paper_date":"2017-07-11","paper":"/paper/value-prediction-network","paper_url":"http://arxiv.org/abs/1707.03497v2","paper_title":"Value Prediction Network","code":"https://github.com/junhyukoh/value-prediction-network","n_code_links":2,"syntology":null},{"rank_in_archive_order":32,"model":"Nature DQN","metrics":{"Score":"5286.0"},"uses_additional_data":false,"paper_date":"2015-02-25","paper":"/paper/human-level-control-through-deep","paper_url":"https://www.nature.com/articles/nature14236","paper_title":"Human level control through deep reinforcement learning","code":"https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Deep_Reinforcement_Learning_with_Double_Q_learning","n_code_links":8,"syntology":null},{"rank_in_archive_order":33,"model":"UCT","metrics":{"Score":"5132.4"},"uses_additional_data":false,"paper_date":"2012-07-19","paper":"/paper/the-arcade-learning-environment-an-evaluation","paper_url":"http://arxiv.org/abs/1207.4708v2","paper_title":"The Arcade Learning Environment: An Evaluation Platform for General Agents","code":"https://github.com/mgbellemare/Arcade-Learning-Environment","n_code_links":3,"syntology":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":34,"model":"DQN hs","metrics":{"Score":"4216.7"},"uses_additional_data":false,"paper_date":"2015-09-22","paper":"/paper/deep-reinforcement-learning-with-double-q","paper_url":"http://arxiv.org/abs/1509.06461v3","paper_title":"Deep Reinforcement Learning with Double Q-learning","code":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","n_code_links":97,"syntology":{"n_ran":56,"n_unverified":50,"n_samples":106,"n_pointer_only_licence":57}},{"rank_in_archive_order":35,"model":"DNA","metrics":{"Score":"4146"},"uses_additional_data":false,"paper_date":"2022-06-20","paper":"/paper/dna-proximal-policy-optimization-with-a-dual","paper_url":"https://arxiv.org/abs/2206.10027v2","paper_title":"DNA: Proximal Policy Optimization with a Dual Network Architecture","code":"https://github.com/maitchison/PPO","n_code_links":1,"syntology":null},{"rank_in_archive_order":36,"model":"A2C + SIL","metrics":{"Score":"2456.5"},"uses_additional_data":false,"paper_date":"2018-06-14","paper":"/paper/self-imitation-learning","paper_url":"http://arxiv.org/abs/1806.05635v1","paper_title":"Self-Imitation Learning","code":"https://github.com/junhyukoh/self-imitation-learning","n_code_links":4,"syntology":{"n_ran":1,"n_unverified":5,"n_samples":6,"n_pointer_only_licence":3}},{"rank_in_archive_order":37,"model":"A3C FF hs","metrics":{"Score":"2355.4"},"uses_additional_data":false,"paper_date":"2016-02-04","paper":"/paper/asynchronous-methods-for-deep-reinforcement","paper_url":"http://arxiv.org/abs/1602.01783v2","paper_title":"Asynchronous Methods for Deep Reinforcement Learning","code":"https://github.com/ray-project/ray/tree/master/rllib","n_code_links":70,"syntology":{"n_ran":39,"n_unverified":56,"n_samples":95,"n_pointer_only_licence":12}},{"rank_in_archive_order":38,"model":"A3C FF (1 day) hs","metrics":{"Score":"2300.2"},"uses_additional_data":false,"paper_date":"2016-02-04","paper":"/paper/asynchronous-methods-for-deep-reinforcement","paper_url":"http://arxiv.org/abs/1602.01783v2","paper_title":"Asynchronous Methods for Deep Reinforcement Learning","code":"https://github.com/ray-project/ray/tree/master/rllib","n_code_links":70,"syntology":{"n_ran":39,"n_unverified":56,"n_samples":95,"n_pointer_only_licence":12}},{"rank_in_archive_order":39,"model":"DDRL A3C","metrics":{"Score":"1832"},"uses_additional_data":false,"paper_date":"2018-01-09","paper":"/paper/distributed-deep-reinforcement-learning-learn","paper_url":"http://arxiv.org/abs/1801.02852v2","paper_title":"Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes","code":"https://github.com/deepsense-ai/Distributed-BA3C","n_code_links":1,"syntology":null},{"rank_in_archive_order":40,"model":"POP3D","metrics":{"Score":"1807.47"},"uses_additional_data":false,"paper_date":"2018-07-02","paper":"/paper/policy-optimization-with-penalized-point","paper_url":"http://arxiv.org/abs/1807.00442v4","paper_title":"Policy Optimization With Penalized Point Probability Distance: An Alternative To Proximal Policy Optimization","code":"https://github.com/cxxgtxy/POP3D","n_code_links":2,"syntology":null},{"rank_in_archive_order":41,"model":"IMPALA (deep)","metrics":{"Score":"1753.20"},"uses_additional_data":false,"paper_date":"2018-02-05","paper":"/paper/impala-scalable-distributed-deep-rl-with","paper_url":"http://arxiv.org/abs/1802.01561v3","paper_title":"IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures","code":"https://github.com/ray-project/ray/tree/master/rllib","n_code_links":24,"syntology":{"n_ran":16,"n_unverified":18,"n_samples":34,"n_pointer_only_licence":3}},{"rank_in_archive_order":42,"model":"DQN Best","metrics":{"Score":"1740"},"uses_additional_data":false,"paper_date":"2013-12-19","paper":"/paper/playing-atari-with-deep-reinforcement","paper_url":"http://arxiv.org/abs/1312.5602v1","paper_title":"Playing Atari with Deep Reinforcement Learning","code":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","n_code_links":112,"syntology":{"n_ran":56,"n_unverified":61,"n_samples":117,"n_pointer_only_licence":56}},{"rank_in_archive_order":43,"model":"MAC","metrics":{"Score":"1703.4"},"uses_additional_data":false,"paper_date":"2017-09-01","paper":"/paper/mean-actor-critic","paper_url":"http://arxiv.org/abs/1709.00503v2","paper_title":"Mean Actor Critic","code":"https://github.com/kavosh8/MAC","n_code_links":2,"syntology":null},{"rank_in_archive_order":44,"model":"Prior+Duel hs","metrics":{"Score":"1431.2"},"uses_additional_data":false,"paper_date":"2015-09-22","paper":"/paper/deep-reinforcement-learning-with-double-q","paper_url":"http://arxiv.org/abs/1509.06461v3","paper_title":"Deep Reinforcement Learning with Double Q-learning","code":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","n_code_links":97,"syntology":{"n_ran":56,"n_unverified":50,"n_samples":106,"n_pointer_only_licence":57}},{"rank_in_archive_order":45,"model":"ES FF (1 hour) noop","metrics":{"Score":"1390.0"},"uses_additional_data":false,"paper_date":"2017-03-10","paper":"/paper/evolution-strategies-as-a-scalable","paper_url":"http://arxiv.org/abs/1703.03864v2","paper_title":"Evolution Strategies as a Scalable Alternative to Reinforcement Learning","code":"https://github.com/ray-project/ray/tree/master/rllib","n_code_links":23,"syntology":{"n_ran":7,"n_unverified":22,"n_samples":29,"n_pointer_only_licence":1}},{"rank_in_archive_order":46,"model":"A3C LSTM hs","metrics":{"Score":"1326.1"},"uses_additional_data":false,"paper_date":"2016-02-04","paper":"/paper/asynchronous-methods-for-deep-reinforcement","paper_url":"http://arxiv.org/abs/1602.01783v2","paper_title":"Asynchronous Methods for Deep Reinforcement Learning","code":"https://github.com/ray-project/ray/tree/master/rllib","n_code_links":70,"syntology":{"n_ran":39,"n_unverified":56,"n_samples":95,"n_pointer_only_licence":12}},{"rank_in_archive_order":47,"model":"Prior+Duel noop","metrics":{"Score":"931.6"},"uses_additional_data":false,"paper_date":"2015-11-20","paper":"/paper/dueling-network-architectures-for-deep","paper_url":"http://arxiv.org/abs/1511.06581v3","paper_title":"Dueling Network Architectures for Deep Reinforcement Learning","code":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","n_code_links":73,"syntology":{"n_ran":5,"n_unverified":6,"n_samples":11,"n_pointer_only_licence":6}},{"rank_in_archive_order":48,"model":"CGP","metrics":{"Score":"724"},"uses_additional_data":false,"paper_date":"2018-06-14","paper":"/paper/evolving-simple-programs-for-playing-atari","paper_url":"http://arxiv.org/abs/1806.05695v1","paper_title":"Evolving simple programs for playing Atari games","code":"https://github.com/ShuhuaGao/gpFlappyBird","n_code_links":2,"syntology":{"n_ran":0,"n_unverified":9,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":49,"model":"SARSA","metrics":{"Score":"675.5"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":50,"model":"Best Learner","metrics":{"Score":"664.8"},"uses_additional_data":false,"paper_date":"2012-07-19","paper":"/paper/the-arcade-learning-environment-an-evaluation","paper_url":"http://arxiv.org/abs/1207.4708v2","paper_title":"The Arcade Learning Environment: An Evaluation Platform for General Agents","code":"https://github.com/mgbellemare/Arcade-Learning-Environment","n_code_links":3,"syntology":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":51,"model":"Discrete Latent Space World Model (VQ-VAE)","metrics":{"Score":"635"},"uses_additional_data":false,"paper_date":"2020-10-12","paper":"/paper/discrete-latent-space-world-models-for","paper_url":"https://arxiv.org/abs/2010.05767v2","paper_title":"Smaller World Models for Reinforcement Learning","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":52,"model":"Rainbow+SEER","metrics":{"Score":"561.2"},"uses_additional_data":false,"paper_date":"2021-03-04","paper":"/paper/improving-computational-efficiency-in-visual","paper_url":"https://arxiv.org/abs/2103.02886v2","paper_title":"Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings","code":"https://github.com/lili-chen/SEER","n_code_links":1,"syntology":null},{"rank_in_archive_order":53,"model":"CURL","metrics":{"Score":"408"},"uses_additional_data":false,"paper_date":"2020-04-08","paper":"/paper/curl-contrastive-unsupervised-representations","paper_url":"https://arxiv.org/abs/2004.04136v4","paper_title":"CURL: Contrastive Unsupervised Representations for Reinforcement Learning","code":"https://github.com/opendilab/DI-engine","n_code_links":7,"syntology":{"n_ran":6,"n_unverified":2,"n_samples":8,"n_pointer_only_licence":0}},{"rank_in_archive_order":54,"model":"IDVQ + DRSC + XNES","metrics":{"Score":"320"},"uses_additional_data":false,"paper_date":"2018-06-04","paper":"/paper/playing-atari-with-six-neurons","paper_url":"http://arxiv.org/abs/1806.01363v2","paper_title":"Playing Atari with Six Neurons","code":"https://github.com/giuse/DNE","n_code_links":1,"syntology":null},{"rank_in_archive_order":55,"model":"SAC","metrics":{"Score":"211.6"},"uses_additional_data":false,"paper_date":"2019-10-16","paper":"/paper/soft-actor-critic-for-discrete-action","paper_url":"https://arxiv.org/abs/1910.07207v2","paper_title":"Soft Actor-Critic for Discrete Action Settings","code":"https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch","n_code_links":13,"syntology":{"n_ran":12,"n_unverified":6,"n_samples":18,"n_pointer_only_licence":3}},{"rank_in_archive_order":56,"model":"DT","metrics":{"Score":"2.4"},"uses_additional_data":false,"paper_date":"2021-06-02","paper":"/paper/decision-transformer-reinforcement-learning","paper_url":"https://arxiv.org/abs/2106.01345v2","paper_title":"Decision Transformer: Reinforcement Learning via Sequence Modeling","code":"https://github.com/opendilab/DI-engine","n_code_links":20,"syntology":{"n_ran":17,"n_unverified":9,"n_samples":26,"n_pointer_only_licence":6}},{"rank_in_archive_order":57,"model":"IQ-Learn","metrics":{"Return":"2349"},"uses_additional_data":false,"paper_date":"2021-06-23","paper":"/paper/iq-learn-inverse-soft-q-learning-for","paper_url":"https://arxiv.org/abs/2106.12142v4","paper_title":"IQ-Learn: Inverse soft-Q Learning for Imitation","code":"https://github.com/Div99/IQ-Learn","n_code_links":5,"syntology":{"n_ran":2,"n_unverified":1,"n_samples":3,"n_pointer_only_licence":3}}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,885 of the 9,623 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9623,"papers_checked":6885,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":2737},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-25T09:33:49+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":35,"rows_with_any_sample_ran":32,"distinct_papers_with_graph_line":24,"distinct_papers_with_any_sample_ran":21,"samples_over_distinct_papers":{"n_ran":368,"n_unverified":326,"n_samples":694,"n_pointer_only_licence":267,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":715,"n_unverified":639,"n_samples":1354,"n_pointer_only_licence":528,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}