Initial public commit

.gitattributes

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+corpus*.jsonl filter=lfs diff=lfs merge=lfs -text
+corpus*.json filter=lfs diff=lfs merge=lfs -text

.gitignore

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+output-data/
+input-data/
+.direnv/
+.envrc
+__pycache__/

.gitmodules

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+[submodule "library/egg-discrimination/code/EGG"]
+	path = data/egg-discrimination/code/repo
+	url = https://github.com/facebookresearch/EGG
+[submodule "library/generalizations-mu-goodman/code/repo"]
+	path = data/generalizations-mu-goodman/code/repo
+	url = https://github.com/brendon-boldt/emergent-generalization
+[submodule "library/ec-at-scale/emergent_communication_at_scale"]
+	path = data/ec-at-scale/code/emergent_communication_at_scale
+	url = https://github.com/brendon-boldt/emergent_communication_at_scale
+[submodule "library/corpus-transfer-yao-et-al/repo"]
+	path = data/corpus-transfer-yao-et-al/code/repo
+	url = https://github.com/brendon-boldt/ec-nl
+[submodule "library/rlupus/repo"]
+	path = data/rlupus/code/repo
+	url = https://github.com/brendon-boldt/rl_werewolf
+[submodule "library/comon-iccv/code/repo"]
+	path = library/comon-iccv/code/repo
+	url = https://github.com/saimwani/CoMON
+[submodule "library/babyai-sr/code/repo"]
+	path = data/babyai-sr/code/repo
+	url = https://github.com/brendon-boldt/babyai_sr

README.md

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+# Emergent Language Library
+
+
+## Running code
+
+For each emergent language entry, we provide wrapper code to create
+a reproducible environment and run the emergent language-generating code.
+Environments are specified precisely in the `environment.yml` file; if you wish
+to edit the dependencies manually, it may be easier to start with
+`environment.editable.yml` instead, if it exists. 
+
+### Git submodules
+This project uses git submodules to manage external dependencies.  Submodules
+do not always operate in an intuitive way, so we provide a brief explanation of
+how to use them here.  By default, submodules are not "init-ed" which means
+that they will be empty after you clone the project.  If you would like to
+populate a submodule (i.e., the directory pointing to another repo) to see or
+use its code, run `git submodule init path/to/submodule` to mark it as init-ed.
+Second, run `git submodule update` to populated init-ed submodules.  Run `git
+submodule deinit -f path/to/submodule` to make the submodule empty again.

croissant.json

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+{
+  "@context": {
+    "@language": "en",
+    "@vocab": "https://schema.org/",
+    "citeAs": "cr:citeAs",
+    "column": "cr:column",
+    "conformsTo": "dct:conformsTo",
+    "cr": "http://mlcommons.org/croissant/",
+    "rai": "http://mlcommons.org/croissant/RAI/",
+    "data": {
+      "@id": "cr:data",
+      "@type": "@json"
+    },
+    "dataType": {
+      "@id": "cr:dataType",
+      "@type": "@vocab"
+    },
+    "dct": "http://purl.org/dc/terms/",
+    "examples": {
+      "@id": "cr:examples",
+      "@type": "@json"
+    },
+    "extract": "cr:extract",
+    "field": "cr:field",
+    "fileProperty": "cr:fileProperty",
+    "fileObject": "cr:fileObject",
+    "fileSet": "cr:fileSet",
+    "format": "cr:format",
+    "includes": "cr:includes",
+    "isLiveDataset": "cr:isLiveDataset",
+    "jsonPath": "cr:jsonPath",
+    "key": "cr:key",
+    "md5": "cr:md5",
+    "parentField": "cr:parentField",
+    "path": "cr:path",
+    "recordSet": "cr:recordSet",
+    "references": "cr:references",
+    "regex": "cr:regex",
+    "repeated": "cr:repeated",
+    "replace": "cr:replace",
+    "sc": "https://schema.org/",
+    "separator": "cr:separator",
+    "source": "cr:source",
+    "subField": "cr:subField",
+    "transform": "cr:transform"
+  },
+  "@type": "sc:Dataset",
+  "name": "ELCC",
+  "description": "ELCC is a collection of emergent language corpora with accompanying metadata and analyses.",
+  "conformsTo": "http://mlcommons.org/croissant/1.0",
+  "citeAs": "  @misc{boldt2024elcc,\n    title = \"{ELCC}: the Emergent Language Corpus Collection\",\n    author = \"Brendon Boldt and David Mortensen\",\n    year = 2024,\n    url = \"\",\n}",
+  "datePublished": "2024-06-05 22:14:16.853626+00:00",
+  "keywords": [
+    "emergent communication",
+    "emergent language",
+    "corpus"
+  ],
+  "license": "https://creativecommons.org/licenses/by/4.0/",
+  "url": "https://huggingface.co/datasets/bboldt/elcc/",
+  "version": "0.1.0",
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+          "@id": "generalizations-mu-goodman/cub-reference/content",
+          "name": "content",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "data/generalizations-mu-goodman/data/cub-reference/corpus.json"
+            },
+            "extract": {
+              "fileProperty": "content"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "generalizations-mu-goodman/shapeworld-reference",
+      "name": "generalizations-mu-goodman/shapeworld-reference",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "generalizations-mu-goodman/shapeworld-reference/content",
+          "name": "content",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "data/generalizations-mu-goodman/data/shapeworld-reference/corpus.json"
+            },
+            "extract": {
+              "fileProperty": "content"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "generalizations-mu-goodman/shapeworld-set_reference",
+      "name": "generalizations-mu-goodman/shapeworld-set_reference",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "generalizations-mu-goodman/shapeworld-set_reference/content",
+          "name": "content",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "data/generalizations-mu-goodman/data/shapeworld-set_reference/corpus.json"
+            },
+            "extract": {
+              "fileProperty": "content"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "generalizations-mu-goodman/cub-concept",
+      "name": "generalizations-mu-goodman/cub-concept",
+      "field": [
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+          "@id": "generalizations-mu-goodman/cub-concept/content",
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+            }
+          }
+        }
+      ]
+    },
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+      "@type": "cr:RecordSet",
+      "@id": "generalizations-mu-goodman/cub-set_reference",
+      "name": "generalizations-mu-goodman/cub-set_reference",
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+          "@id": "generalizations-mu-goodman/cub-set_reference/content",
+          "name": "content",
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+          "source": {
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+              "@id": "data/generalizations-mu-goodman/data/cub-set_reference/corpus.json"
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+            "extract": {
+              "fileProperty": "content"
+            }
+          }
+        }
+      ]
+    },
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+          "@id": "generalizations-mu-goodman/shapeworld-concept/content",
+          "name": "content",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "data/generalizations-mu-goodman/data/shapeworld-concept/corpus.json"
+            },
+            "extract": {
+              "fileProperty": "content"
+            }
+          }
+        }
+      ]
+    },
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+      "@type": "cr:RecordSet",
+      "@id": "rlupus/21-player.run-2",
+      "name": "rlupus/21-player.run-2",
+      "field": [
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+          "@id": "rlupus/21-player.run-2/content",
+          "name": "content",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "data/rlupus/data/21-player.run-2/corpus.json"
+            },
+            "extract": {
+              "fileProperty": "content"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "rlupus/9-player.run-0",
+      "name": "rlupus/9-player.run-0",
+      "field": [
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+          "@id": "rlupus/9-player.run-0/content",
+          "name": "content",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "data/rlupus/data/9-player.run-0/corpus.json"
+            },
+            "extract": {
+              "fileProperty": "content"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "rlupus/9-player.run-3",
+      "name": "rlupus/9-player.run-3",
+      "field": [
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+          "@type": "cr:Field",
+          "@id": "rlupus/9-player.run-3/content",
+          "name": "content",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "data/rlupus/data/9-player.run-3/corpus.json"
+            },
+            "extract": {
+              "fileProperty": "content"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "rlupus/21-player.run-0",
+      "name": "rlupus/21-player.run-0",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "rlupus/21-player.run-0/content",
+          "name": "content",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "data/rlupus/data/21-player.run-0/corpus.json"
+            },
+            "extract": {
+              "fileProperty": "content"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "rlupus/9-player.run-2",
+      "name": "rlupus/9-player.run-2",
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+          "@id": "rlupus/9-player.run-2/content",
+          "name": "content",
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+          "source": {
+            "fileObject": {
+              "@id": "data/rlupus/data/9-player.run-2/corpus.json"
+            },
+            "extract": {
+              "fileProperty": "content"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "rlupus/21-player.run-1",
+      "name": "rlupus/21-player.run-1",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "rlupus/21-player.run-1/content",
+          "name": "content",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "data/rlupus/data/21-player.run-1/corpus.json"
+            },
+            "extract": {
+              "fileProperty": "content"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "rlupus/9-player.run-1",
+      "name": "rlupus/9-player.run-1",
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+          "@id": "rlupus/9-player.run-1/content",
+          "name": "content",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "data/rlupus/data/9-player.run-1/corpus.json"
+            },
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+            }
+          }
+        }
+      ]
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+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "egg-discrimination/6-attr_6-val_9-dist_10-vocab_10-len/content",
+          "name": "content",
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+          "source": {
+            "fileObject": {
+              "@id": "data/egg-discrimination/data/6-attr_6-val_9-dist_10-vocab_10-len/corpus.json"
+            },
+            "extract": {
+              "fileProperty": "content"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
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+      "name": "egg-discrimination/8-attr_8-val_3-dist_10-vocab_10-len",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "egg-discrimination/8-attr_8-val_3-dist_10-vocab_10-len/content",
+          "name": "content",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "data/egg-discrimination/data/8-attr_8-val_3-dist_10-vocab_10-len/corpus.json"
+            },
+            "extract": {
+              "fileProperty": "content"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "egg-discrimination/4-attr_4-val_3-dist_10-vocab_10-len",
+      "name": "egg-discrimination/4-attr_4-val_3-dist_10-vocab_10-len",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "egg-discrimination/4-attr_4-val_3-dist_10-vocab_10-len/content",
+          "name": "content",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "data/egg-discrimination/data/4-attr_4-val_3-dist_10-vocab_10-len/corpus.json"
+            },
+            "extract": {
+              "fileProperty": "content"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "egg-discrimination/6-attr_6-val_3-dist_10-vocab_10-len",
+      "name": "egg-discrimination/6-attr_6-val_3-dist_10-vocab_10-len",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "egg-discrimination/6-attr_6-val_3-dist_10-vocab_10-len/content",
+          "name": "content",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "data/egg-discrimination/data/6-attr_6-val_3-dist_10-vocab_10-len/corpus.json"
+            },
+            "extract": {
+              "fileProperty": "content"
+            }
+          }
+        }
+      ]
+    }
+  ]
+}

data/babyai-sr/code/.gitignore

@@ -0,0 +1 @@
+/save/

data/babyai-sr/code/environment.editable.yml

@@ -0,0 +1,14 @@
+name: babyai-sr
+channels:
+  - pytorch
+  - conda-forge
+dependencies:
+  - python==3.7.*
+  - matplotlib<=3.1.3
+  - pytorch==1.5.1
+  - tensorboardx
+  - numpy==1.15.4
+  - pip:
+    - gym==0.9.6
+    - gym_minigrid@git+https://github.com/thomasaunger/gym-minigrid
+    - babyai@git+https://github.com/brendon-boldt/babyai-thomasaunger

data/babyai-sr/code/environment.yml

@@ -0,0 +1,94 @@
+name: babyai-sr
+channels:
+- pytorch
+- conda-forge
+dependencies:
+- _libgcc_mutex=0.1=conda_forge
+- _openmp_mutex=4.5=2_kmp_llvm
+- blas=2.122=mkl
+- blas-devel=3.9.0=22_linux64_mkl
+- ca-certificates=2024.2.2=hbcca054_0
+- cudatoolkit=10.2.89=hdec6ad0_13
+- cycler=0.11.0=pyhd8ed1ab_0
+- dbus=1.13.6=hfdff14a_1
+- expat=2.6.2=h59595ed_0
+- fontconfig=2.14.2=h14ed4e7_0
+- freetype=2.12.1=h267a509_2
+- gettext=0.22.5=h59595ed_2
+- gettext-tools=0.22.5=h59595ed_2
+- glib=2.66.3=h58526e2_0
+- gst-plugins-base=1.14.5=h0935bb2_2
+- gstreamer=1.14.5=h36ae1b5_2
+- icu=64.2=he1b5a44_1
+- jpeg=9e=h0b41bf4_3
+- kiwisolver=1.4.4=py37h7cecad7_0
+- ld_impl_linux-64=2.40=hf3520f5_1
+- libasprintf=0.22.5=h661eb56_2
+- libasprintf-devel=0.22.5=h661eb56_2
+- libblas=3.9.0=22_linux64_mkl
+- libcblas=3.9.0=22_linux64_mkl
+- libclang=9.0.1=default_hb4e5071_5
+- libexpat=2.6.2=h59595ed_0
+- libffi=3.2.1=he1b5a44_1007
+- libgcc-ng=13.2.0=h77fa898_7
+- libgettextpo=0.22.5=h59595ed_2
+- libgettextpo-devel=0.22.5=h59595ed_2
+- libgfortran-ng=13.2.0=h69a702a_7
+- libgfortran5=13.2.0=hca663fb_7
+- libglib=2.66.3=hbe7bbb4_0
+- libiconv=1.17=hd590300_2
+- liblapack=3.9.0=22_linux64_mkl
+- liblapacke=3.9.0=22_linux64_mkl
+- libllvm9=9.0.1=default_hc23dcda_7
+- libpng=1.6.43=h2797004_0
+- libprotobuf=3.20.1=h6239696_4
+- libsqlite=3.45.3=h2797004_0
+- libstdcxx-ng=13.2.0=hc0a3c3a_7
+- libuuid=2.38.1=h0b41bf4_0
+- libxcb=1.16=hd590300_0
+- libxkbcommon=0.10.0=he1b5a44_0
+- libxml2=2.9.10=hee79883_0
+- libzlib=1.2.13=h4ab18f5_6
+- llvm-openmp=18.1.6=ha31de31_0
+- matplotlib=3.1.3=py37_0
+- matplotlib-base=3.1.3=py37h250f245_0
+- mkl=2024.1.0=ha957f24_693
+- mkl-devel=2024.1.0=ha770c72_693
+- mkl-include=2024.1.0=ha957f24_693
+- ncurses=6.5=h59595ed_0
+- ninja=1.12.1=h297d8ca_0
+- nspr=4.35=h27087fc_0
+- nss=3.100=hca3bf56_0
+- numpy=1.15.4=py37h8b7e671_1002
+- openssl=1.1.1w=hd590300_0
+- pcre=8.45=h9c3ff4c_0
+- pip=24.0=pyhd8ed1ab_0
+- protobuf=3.20.1=py37hd23a5d3_0
+- pthread-stubs=0.4=h36c2ea0_1001
+- pyparsing=3.1.2=pyhd8ed1ab_0
+- pyqt=5.12.3=py37h8685d9f_3
+- python=3.7.8=h8bdb77d_2_cpython
+- python-dateutil=2.9.0=pyhd8ed1ab_0
+- python_abi=3.7=4_cp37m
+- pytorch=1.5.1=py3.7_cuda10.2.89_cudnn7.6.5_0
+- qt=5.12.5=hd8c4c69_1
+- readline=8.2=h8228510_1
+- setuptools=69.0.3=pyhd8ed1ab_0
+- six=1.16.0=pyh6c4a22f_0
+- sqlite=3.45.3=h2c6b66d_0
+- tbb=2021.7.0=h924138e_0
+- tensorboardx=2.5.1=pyhd8ed1ab_0
+- tk=8.6.13=noxft_h4845f30_101
+- tornado=6.2=py37h540881e_0
+- typing-extensions=4.7.1=hd8ed1ab_0
+- typing_extensions=4.7.1=pyha770c72_0
+- wheel=0.42.0=pyhd8ed1ab_0
+- xorg-libxau=1.0.11=hd590300_0
+- xorg-libxdmcp=1.1.3=h7f98852_0
+- xz=5.2.6=h166bdaf_0
+- zlib=1.2.13=h4ab18f5_6
+- zstd=1.5.6=ha6fb4c9_0
+- pip:
+  - gym==0.9.6
+  - gym_minigrid@git+https://github.com/thomasaunger/gym-minigrid
+  - babyai@git+https://github.com/brendon-boldt/babyai-thomasaunger

data/babyai-sr/code/helper.py

@@ -0,0 +1,227 @@
+from argparse import Namespace
+import json
+from pathlib import Path
+from typing import Tuple, List, Dict
+
+import gym
+import torch
+import numpy as np
+
+import babyai.utils as utils
+import babyai_sr.utils as utils_sr
+from babyai_sr.arguments import ArgumentParser
+from babyai_sr.rl.algos import TestAlgo, BaseAlgo
+from babyai_sr.rl.utils import ParallelEnv
+import util as lib_util
+
+##### based on repo/babyai_sr/rl/algos/test.py #####
+
+SENDER = 0
+RECEIVER = 1
+
+
+def extract_messages(message, done, active) -> List[List[int]]:
+    argmax_messages = message.argmax(-1)[:, :, SENDER]
+    done_np = done.cpu().numpy()
+    done_idxs = np.argwhere(done_np)
+    message_list = []
+    for i in range(done_idxs.shape[0]):
+        prev_end = done_idxs[i - 1] if i > 0 else [0, -1]
+        end = done_idxs[i]
+        # If we are spanning the border between procs, adjust the prev_end
+        # to start at the end's proc.
+        if prev_end[0] != end[0]:
+            prev_end = [end[0], -1]
+        # Take messages from the given proc and
+        indexer = np.s_[prev_end[0], prev_end[1] + 1 : (end[1] + 1)]
+        ep_messages = argmax_messages[indexer]
+        # We only want messages for when the sender is active since the
+        # message is just repeated for the receiver until the sender is
+        # active again.
+        ep_active = active[indexer][:, SENDER]
+        final_ep_messages = ep_messages[ep_active]
+        message_list.append(final_ep_messages.cpu().tolist())
+    return message_list
+
+
+class TestAlgo(BaseAlgo):
+    def __init__(
+        self,
+        env,
+        models,
+        num_frames_per_proc=40,
+        discount=0.99,
+        gae_lambda=0.99,
+        preprocess_obss=None,
+        reshape_reward=None,
+        use_comm=True,
+        conventional=False,
+        argmax=True,
+    ):
+
+        super().__init__(
+            env,
+            models,
+            num_frames_per_proc,
+            discount,
+            gae_lambda,
+            preprocess_obss,
+            reshape_reward,
+            use_comm,
+            conventional,
+            argmax,
+        )
+
+    def collect_episodes(self, episodes):
+        # Collect experiences.
+        exps, _ = self.collect_experiences()
+        batch = 1
+
+        shape_prefix = (self.num_procs, self.num_frames_per_proc)
+
+        active = exps.active.view(*shape_prefix, *exps.active.shape[1:])
+        done = exps.done.view(*shape_prefix, *exps.done.shape[1:])
+        message = exps.message.view(*shape_prefix, *exps.message.shape[1:])
+        reward = exps.reward.view(*shape_prefix, *exps.reward.shape[1:])
+
+        log = {
+            "return_per_episode": [],
+        }
+
+        proc = 0
+        frame = [0] * self.num_procs
+        episodes_done = 0
+        while True:
+            if done[proc, frame[proc]]:
+                episodes_done += 1
+                log["return_per_episode"].append(
+                    reward[proc, frame[proc], RECEIVER].item()
+                )
+                if episodes_done == episodes:
+                    break
+                frame[proc] += 1
+                proc = (proc + 1) % self.num_procs
+            else:
+                frame[proc] += 1
+
+            if frame[proc] == batch * self.num_frames_per_proc:
+                exps, _ = self.collect_experiences()
+                batch += 1
+
+                next_active = exps.active.view(*shape_prefix, *exps.active.shape[1:])
+                next_done = exps.done.view(*shape_prefix, *exps.done.shape[1:])
+                next_message = exps.message.view(*shape_prefix, *exps.message.shape[1:])
+                next_reward = exps.reward.view(*shape_prefix, *exps.reward.shape[1:])
+
+                active = torch.cat((active, next_active), 1)
+                done = torch.cat((done, next_done), 1)
+                message = torch.cat((message, next_message), 1)
+                reward = torch.cat((reward, next_reward), 1)
+
+        message_list = extract_messages(message, done, active)
+
+        return message_list, log
+
+
+##### based on repo/babyai_sr/scripts/test_rl.py #####
+
+
+def parse_args() -> Namespace:
+    parser = ArgumentParser()
+    parser.add_argument("--name", help="name of the configuration")
+    parser.add_argument("--sender", default=None, help="name of the sender (REQUIRED)")
+    parser.add_argument(
+        "--receiver", default=None, help="name of the receiver (REQUIRED)"
+    )
+    parser.add_argument(
+        "--sample",
+        action="store_true",
+        default=False,
+        help="sample messages instead of using argmax",
+    )
+    parser.add_argument(
+        "--episodes",
+        type=int,
+        default=1000,
+        help="number of episodes to test on (default: 1000)",
+    )
+    parser.add_argument("--len-message", default=None, help="dummy")
+    parser.add_argument("--num-symbols", default=None, help="dummy")
+    return parser.parse_args()
+
+
+def get_episodes(args: Namespace) -> Tuple[List, Dict]:
+    utils.seed(args.seed)
+    envs = []
+    for i in range(args.procs):
+        env = gym.make(args.env)
+        env.seed(100 * args.seed + i)
+        envs.append(env)
+    penv = ParallelEnv(
+        envs, args.n, args.conventional, args.archimedean, args.informed_sender
+    )
+
+    sender = utils.load_model(args.sender)
+    receiver = utils.load_model(args.receiver)
+    sender.eval()
+    receiver.eval()
+    if torch.cuda.is_available():
+        sender.cuda()
+        receiver.cuda()
+
+    reshape_reward = lambda _0, _1, reward, _2: args.reward_scale * reward
+    obss_preprocessor = utils_sr.MultiObssPreprocessor(
+        [args.sender, args.receiver], [envs[0].observation_space] * 2
+    )
+    test_algo = TestAlgo(
+        penv,
+        [sender, receiver],
+        args.frames_per_proc,
+        args.discount,
+        args.gae_lambda,
+        obss_preprocessor,
+        reshape_reward,
+        not args.no_comm,
+        args.conventional,
+        not args.sample,
+    )
+
+    return test_algo.collect_episodes(args.episodes)
+
+
+def write_results(args: Namespace, message_list: List, logs: Dict) -> None:
+    out_path = Path(f"../../data/{args.name}")
+    out_path.mkdir(exist_ok=True, parents=True)
+
+    return_per_episode = utils.synthesize(logs["return_per_episode"])
+    success_per_episode = utils.synthesize(
+        [1 if r > 0 else 0 for r in logs["return_per_episode"]]
+    )
+
+    metrics = {
+        "success_rate": success_per_episode["mean"],
+        "success_rate_std": success_per_episode["std"],
+        "reward_per_episode": return_per_episode["mean"],
+        "reward_per_episode_std": return_per_episode["std"],
+    }
+    lib_util.write_system_metrics(out_path, metrics)
+
+    with (out_path / "corpus.jsonl").open("w") as fo:
+        for message in message_list:
+            flat = [x for y in message for x in y]
+            json.dump(flat, fo, indent=None)
+            fo.write("\n")
+    with (out_path / "corpus.structured.jsonl").open("w") as fo:
+        for message in message_list:
+            json.dump(message, fo, indent=None)
+            fo.write("\n")
+
+
+def main() -> None:
+    args = parse_args()
+    message_list, logs = get_episodes(args)
+    write_results(args, message_list, logs)
+
+
+if __name__ == "__main__":
+    main()

data/babyai-sr/code/repo

@@ -0,0 +1 @@
+Subproject commit d4a3cb757614bc3b343608f751500eeb31f7db7a

data/babyai-sr/code/run.sh

@@ -0,0 +1,65 @@
+#!/usr/bin/env bash
+
+set -xe
+
+export PYTHONPATH=$(realpath repo/)
+
+args_arr=(
+  "--name GoToObj --n 8 --env BabyAI-GoToObj-v0"
+  "--name GoToObjLocked --n 8 --env BabyAI-GoToObjLocked-v0"
+
+  "--name GoToObjUnlocked-freq_1 --n 1 --env BabyAI-GoToObjUnlocked-v0"
+  "--name GoToObjUnlocked-freq_2 --n 2 --env BabyAI-GoToObjUnlocked-v0"
+  "--name GoToObjUnlocked-freq_4 --n 4 --env BabyAI-GoToObjUnlocked-v0"
+  "--name GoToObjUnlocked --n 8 --env BabyAI-GoToObjUnlocked-v0"
+  "--name GoToObjUnlocked-freq_32 --n 32 --env BabyAI-GoToObjUnlocked-v0"
+  "--name GoToObjLocked_ambiguous-freq_1 --n 1 --env BabyAI-GoToObjLocked_ambiguous-v0"
+  "--name GoToObjLocked_ambiguous-freq_2 --n 2 --env BabyAI-GoToObjLocked_ambiguous-v0"
+  "--name GoToObjLocked_ambiguous-freq_4 --n 4 --env BabyAI-GoToObjLocked_ambiguous-v0"
+  "--name GoToObjLocked_ambiguous --n 8 --env BabyAI-GoToObjLocked_ambiguous-v0"
+  "--name GoToObjLocked_ambiguous-freq_32 --n 32 --env BabyAI-GoToObjLocked_ambiguous-v0"
+
+  "--name GoToObjUnlocked-msg_4 --len-message 4 --num-symbols 4 --n 8 --env BabyAI-GoToObjUnlocked-v0"
+  "--name GoToObjUnlocked-msg_16 --len-message 16 --num-symbols 16 --n 8 --env BabyAI-GoToObjUnlocked-v0"
+  "--name GoToObjUnlocked-msg_32 --len-message 32 --num-symbols 32 --n 8 --env BabyAI-GoToObjUnlocked-v0"
+  "--name GoToObjLocked_ambiguous-msg_4 --len-message 4 --num-symbols 4 --n 8 --env BabyAI-GoToObjLocked_ambiguous-v0"
+  "--name GoToObjLocked_ambiguous-msg_16 --len-message 16 --num-symbols 16 --n 8 --env BabyAI-GoToObjLocked_ambiguous-v0"
+  "--name GoToObjLocked_ambiguous-msg_32 --len-message 32 --num-symbols 32 --n 8 --env BabyAI-GoToObjLocked_ambiguous-v0"
+)
+
+function get_model_path {
+  sr=$1
+  mod=$(ls models | grep ".*ppo_expert.*$sr" | sort | tail -n1)
+  echo -n $mod
+}
+
+for idx in $(seq 0 $((${#args_arr[@]} - 1))); do
+  cd repo
+    args=${args_arr[$idx]}
+    # Remove the leading --name arg which is not in the train_rl.py script.
+    train_args=$(echo $args | cut -d' ' -f3-)
+    name=$(echo $args | cut -d' ' -f2)
+
+    # Be careful about running this in parallel because the code does not use
+    # assign unique names to configurations, so get_model_path might get the
+    # wrong path if multiple instances of this are run in parallel.
+    eval python scripts/train_rl.py \
+      --seed 1 \
+      --frames 5e7 \
+      --tb \
+      $train_args
+
+    orig_sender=$(get_model_path sender)
+    orig_receiver=$(get_model_path receiver)
+    sender=$name-sender
+    receiver=$name-receiver
+    mv -n models/{$orig_sender,$sender}
+    mv -n models/{$orig_receiver,$receiver}
+
+    eval python ../helper.py \
+      --episodes 5000 \
+      --sender $sender \
+      --receiver $receiver \
+      $args
+  cd ..
+done

data/babyai-sr/code/util.py

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+../../../util/util.py

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+      "EoS Token Present": false,
+      "EoS Padding": false
+    }
+  }
+}

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