Add JSON files for ECS metadata

data/babyai-sr/metadata.json

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+[{"origin":{"source":"https://github.com/brendon-boldt/babyai_sr","upstream_source":"https://github.com/thomasaunger/babyai_sr","paper":"https://arxiv.org/abs/2001.01772"},"system":{"game_type":"navigation","game_subtype":"grid","data_source":"synthetic","observation_continuous":false,"observation_type":"vector","seeding_available":true,"multi_step":true,"multi_utterance":true,"more_than_2_agents":false,"symmetric_agents":false,"variants":{"GoToObj":{"env":"GoToObj"},"GoToObjLocked":{"env":"GoToObjLocked"},"GoToObjLocked_ambiguous":{"env":"GoToObjLocked_ambiguous"},"GoToObjLocked_ambiguous-freq_1":{"env":"GoToObjLocked_ambiguous","message_frequency":1},"GoToObjLocked_ambiguous-freq_2":{"env":"GoToObjLocked_ambiguous","message_frequency":2},"GoToObjLocked_ambiguous-freq_32":{"env":"GoToObjLocked_ambiguous","message_frequency":32},"GoToObjLocked_ambiguous-freq_4":{"env":"GoToObjLocked_ambiguous","message_frequency":4},"GoToObjLocked_ambiguous-msg_16":{"env":"GoToObjLocked_ambiguous","message_length":16,"vocab_size":16},"GoToObjLocked_ambiguous-msg_32":{"env":"GoToObjLocked_ambiguous","message_length":32,"vocab_size":32},"GoToObjLocked_ambiguous-msg_4":{"env":"GoToObjLocked_ambiguous","message_length":4,"vocab_size":4},"GoToObjUnlocked":{"env":"GoToObjUnlocked"},"GoToObjUnlocked-freq_1":{"env":"GoToObjUnlocked","message_frequency":1},"GoToObjUnlocked-freq_2":{"env":"GoToObjUnlocked","message_frequency":2},"GoToObjUnlocked-freq_32":{"env":"GoToObjUnlocked","message_frequency":32},"GoToObjUnlocked-freq_4":{"env":"GoToObjUnlocked","message_frequency":4},"GoToObjUnlocked-msg_16":{"env":"GoToObjUnlocked","message_length":16,"vocab_size":16},"GoToObjUnlocked-msg_32":{"env":"GoToObjUnlocked","message_length":32,"vocab_size":32},"GoToObjUnlocked-msg_4":{"env":"GoToObjUnlocked","message_length":4,"vocab_size":4}}},"notes":"Default\n  message_frequency is every 8 steps,\n  message_length is 8,\n  vocab_size is 8.\n"}]

data/corpus-transfer-yao-et-al/metadata.json

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+[{"origin":{"source":"https://github.com/brendon-boldt/ec-nl","upstream_source":"https://github.com/ysymyth/ec-nl","paper":"https://openreview.net/forum?id=49A1Y6tRhaq"},"system":{"game_type":"signalling","game_subtype":"discrimination","observation_type":"image","observation_continuous":true,"data_source":"natural","seeding_available":true,"symmetric_agents":false,"multi_step":false,"multi_utterance":false,"more_than_2_agents":false,"variants":{"coco_2014":{"dataset":"coco_2014"},"cc":{"dataset":"conceptual_captions"}}}}]

data/ec-at-scale/metadata.json

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+[{"origin":{"source":"https://github.com/brendon-boldt/emergent_communication_at_scale","upstream_source":"https://github.com/google-deepmind/emergent_communication_at_scale","paper":"https://openreview.net/forum?id=AUGBfDIV9rL"},"system":{"game_type":"signalling","data_source":"natural","game_subtype":"discrimination","observation_type":"image","observation_continuous":true,"seeding_available":true,"multi_step":false,"more_than_2_agents":true,"multi_utterance":false,"symmetric_agents":false,"variants":{"imagenet-1x10":{"n_receivers":10,"n_senders":1},"imagenet-10x10":{"n_receivers":10,"n_senders":10},"imagenet-5x5":{"n_receivers":5,"n_senders":5},"imagenet-1x1":{"n_receivers":1,"n_senders":1},"imagenet-10x1":{"n_receivers":1,"n_senders":10}}}}]

data/egg-discrimination/metadata.json

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+[{"origin":{"source":"https://github.com/facebookresearch/EGG"},"system":{"game_type":"signalling","game_subtype":"discrimination","observation_type":"vector","observation_continuous":false,"multi_step":false,"data_source":"synthetic","seeding_available":true,"symmetric_agents":false,"multi_utterance":false,"more_than_2_agents":false,"variants":{"4-attr_4-val_3-dist_0-seed":{"seed":0,"n_attributes":4,"n_values":4,"n_distractors":3},"4-attr_4-val_3-dist_1-seed":{"seed":1,"n_attributes":4,"n_values":4,"n_distractors":3},"4-attr_4-val_3-dist_2-seed":{"seed":2,"n_attributes":4,"n_values":4,"n_distractors":3},"6-attr_6-val_3-dist_0-seed":{"seed":0,"n_attributes":6,"n_values":6,"n_distractors":3},"6-attr_6-val_3-dist_1-seed":{"seed":1,"n_attributes":6,"n_values":6,"n_distractors":3},"6-attr_6-val_3-dist_2-seed":{"seed":2,"n_attributes":6,"n_values":6,"n_distractors":3},"6-attr_6-val_9-dist_0-seed":{"seed":0,"n_attributes":6,"n_values":6,"n_distractors":9},"6-attr_6-val_9-dist_1-seed":{"seed":1,"n_attributes":6,"n_values":6,"n_distractors":9},"6-attr_6-val_9-dist_2-seed":{"seed":2,"n_attributes":6,"n_values":6,"n_distractors":9},"8-attr_8-val_3-dist_0-seed":{"n_attributes":8,"n_values":8,"n_distractors":3},"8-attr_8-val_3-dist_1-seed":{"n_attributes":8,"n_values":8,"n_distractors":3},"8-attr_8-val_3-dist_2-seed":{"n_attributes":8,"n_values":8,"n_distractors":3}}}}]

data/egg-reconstruction/metadata.json

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+[{"origin":{"source":"https://github.com/facebookresearch/EGG"},"system":{"game_type":"signalling","game_subtype":"reconstruction","observation_type":"vector","observation_continuous":false,"multi_step":false,"data_source":"synthetic","seeding_available":true,"multi_utterance":false,"symmetric_agents":false,"more_than_2_agents":false,"variants":{"4-attr_4-val_10-vocab_10-len":{"n_attributes":4,"n_values":4},"6-attr_6-val_10-vocab_10-len":{"n_attributes":6,"n_values":6},"8-attr_8-val_10-vocab_10-len":{"n_attributes":8,"n_values":8}}}}]

data/generalizations-mu-goodman/metadata.json

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+[{"origin":{"source":"https://github.com/brendon-boldt/emergent-generalization","upstream_source":"https://github.com/jayelm/emergent-generalization","paper":"https://openreview.net/forum?id=yq5MYHVaClG"},"system":{"game_type":"signalling","game_subtype":"discrimination","observation_type":"image","observation_continuous":true,"multi_step":false,"seeding_available":false,"data_source":"synthetic","multi_utterance":false,"more_than_2_agents":false,"symmetric_agents":false,"variants":{"shapeworld-reference":{},"shapeworld-set_reference":{},"shapeworld-concept":{},"cub-reference":{"data_source":"natural"},"cub-set_reference":{"data_source":"natural"},"cub-concept":{"data_source":"natural"}}},"notes":"The sampled output from this environment has both \"train\" (seen) and \"test\" (unseen) values.  For now, we are just using the previously seen examples as we expect them to have better performance.  The unseen examples could be included in the future (as separate corpora).\n"}]

data/nav-to-center/metadata.json

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+[{"origin":{"source":"https://github.com/brendon-boldt/filex-emergent-language","paper":"https://openreview.net/forum?id=49A1Y6tRhaq"},"system":{"game_type":"navigation","game_subtype":"None","observation_type":"vector","observation_continuous":true,"data_source":"synthetic","seeding_available":false,"symmetric_agents":false,"multi_step":true,"multi_utterance":true,"more_than_2_agents":false,"variants":{"lexicon_size_11":{"vocab_size":11},"lexicon_size_118":{"vocab_size":118},"lexicon_size_17":{"vocab_size":17},"lexicon_size_174":{"vocab_size":114},"lexicon_size_25":{"vocab_size":25},"lexicon_size_255":{"vocab_size":255},"lexicon_size_37":{"vocab_size":37},"lexicon_size_54":{"vocab_size":54},"lexicon_size_7":{"vocab_size":7},"lexicon_size_80":{"vocab_size":80},"temperature_0.1":{"gumbel_softmax_temperature":0.1},"temperature_0.167":{"gumbel_softmax_temperature":0.167},"temperature_0.278":{"gumbel_softmax_temperature":0.278},"temperature_0.464":{"gumbel_softmax_temperature":0.464},"temperature_0.774":{"gumbel_softmax_temperature":0.774},"temperature_10":{"gumbel_softmax_temperature":10},"temperature_1.29":{"gumbel_softmax_temperature":1.29},"temperature_2.15":{"gumbel_softmax_temperature":2.15},"temperature_3.59":{"gumbel_softmax_temperature":3.59},"temperature_5.99":{"gumbel_softmax_temperature":5.99}}}}]

data/rlupus/metadata.json

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+[{"origin":{"source":"https://github.com/brendon-boldt/rl_werewolf","upstream_source":"https://github.com/nicofirst1/rl_werewolf","paper":"https://arxiv.org/abs/2106.05018"},"system":{"game_type":"conversation","game_subtype":"social_deduction","observation_type":"vector","observation_continuous":false,"multi_step":true,"data_source":null,"seeding_available":false,"symmetric_agents":true,"more_than_2_agents":true,"multi_utterance":true,"variants":{"9-player.run-0":{"message_length":9,"vocab_size":9,"note":"reached 0.88 WR (win rate) in 38 hours (no automatic stopping)"},"9-player.run-1":{"message_length":9,"vocab_size":9,"note":"reached 0.44 WR, stopped at 48 hours"},"9-player.run-2":{"message_length":9,"vocab_size":9,"note":"reached 0.44 WR, stopped at 48 hours"},"9-player.run-3":{"message_length":9,"vocab_size":9,"note":"reached 0.44 WR, stopped at 37 hours"},"21-player.run-0":{"message_length":21,"vocab_size":21,"note":"reached 0.88 WR in 34 hours (no automatic stopping)"},"21-player.run-1":{"message_length":21,"vocab_size":21,"note":"reached 0.55 WR, stopped at 48 hours"},"21-player.run-2":{"message_length":21,"vocab_size":21,"note":"reached 0.85 WR in 28 hours"}}},"notes":"This environment is somewhat unstable and does not provide random seeding, so reproducing exact results is tricky.  Nevertheless, environment converge about half of the time (i.e., reach 85% villager win rate before 48 hours of training). 9 player (~11M steps per hour). 21 player (~4M steps per hour).\n"}]