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env/*
!env/README.md
examples/submission/test_submission/env/*
!examples/submission/test_submission/env/README.md
examples/env/*
!examples/env/README.md
# Tensorflow Model Info
models/
summaries/
/.idea
__pycache__/
UnitySDK.log
venv/
*/venv
/dev
build/
dist/
logs/
# Environemnt logfile
*Project.log
# Builds
*.apk
*.unitypackage
*.app
*.exe
*.x86_64
*.x86
# Mac hidden files
*.DS_Store
*/.ipynb_checkpoints
*/.idea
*.pyc
*.idea/misc.xml
*.idea/modules.xml
*.idea/
*.iml
*.cache
*/build/
*/dist/
*.egg-info*
*.eggs*
*.gitignore.swp
# VSCode hidden files
*.vscode/
.DS_Store
.ipynb_checkpoints
# pytest cache
*.pytest_cache/
# Ignore PyPi build files.
dist/
build/
# Python virtual environment
venv/
.mypy_cache/
README.md
View file @ 83479aa8
# Animal-AI Olympics
## The competition is now over
Congratulations to our winners!
1st: Trrrrr
2nd: ironbar
3rd: sirius
Results [here](http://animalaiolympics.com)
Full paper, release of testing configurations, results analsysis and write-up and WBA prize announcment to come.
We will also reopen submissions in the coming days in order to allow you to keep working on the Animal-AI Olympics until we disclose the test sets, these new submissions will not count as part of the competition.
<p align="center">
<img height="300" src="documentation/PrefabsPictures/steampunkFOURcrop.png">
</p>
**July 1st - November 1st**
The Animal-AI Olympics is an AI competition with tests inspired by animal cognition. Participants are given a small environment with just seven different classes of objects that can be placed inside. In each test, the agent needs to retrieve the food in the environment, but to do so there are obstacles to overcome, ramps to climb, boxes to push, and areas that must be avoided. The real challenge is that we don't provide the tests in advance. It's up to you to explore the possibilities with the environment and build interesting configurations that can help create an agent that understands how the environment's physics work and the affordances that it has. The final submission should be an agent capable of robust food retrieval behaviour similar to that of many kinds of animals. We know the animals can pass these tests, it's time to see if AI can too.
## Prizes $32,000 (equivalent value)
* Overall Prizes
* 1st place overall: **$7,500 total value** - $6,500 with up to $1,000 travel to speak at NeurIPS 2019.
* 2nd place overall: **$6,000 total value** - $5,000 with up to $1,000 travel to speak at NeurIPS 2019.
* 3rd place overall: **$1,500**.
* WBA-Prize: **$5,000 total value** - $4,000 with up to $1,000 travel to speak at NeurIPS 2019
* Category Prizes: **$200** For best performance in each category (cannot combine with other prizes - max 1 per team).
* ~~**Mid-way AWS Research Credits**: The top 20 entries as of **September 1st** will be awarded **$500** of AWS credits.~~ (already awarded)
See [competition launch page](https://mdcrosby.com/blog/animalailaunch.html) and official rules for further details.
**Important** Please check the competition rules [here](http://animalaiolympics.com/rules.html). **To submit to the competition and be considered for prizes you must also fill in [this form](https://forms.gle/PKCgp2JAWvjf4c9i6)**. Entry to the competition ([via EvalAI](https://evalai.cloudcv.org/web/challenges/challenge-page/396/overview)) constitutes agreement with all competition rules.
## Overview
Here you will find all the code needed to compete in this new challenge. This repo contains **the training environment** (v1.0) that will be used for the competition. Information for entering can be found in the [submission documentation](documentation/submission.md). Please check back during the competition for minor bug-fixes and updates, but as of v1.0 the major features and contents are set in place.
This repo contains **the training environment** (v1.0) that will be used for the competition. Information for entering can be found in the [submission documentation](documentation/submission.md). Please check back during the competition for minor bug-fixes and updates, but as of v1.0 the major features and contents are set in place.
For more information on the competition itself and to stay updated with any developments, head to the
[Competition Website](http://www.animalaiolympics.com/) and follow [@MacroPhilosophy](https://twitter.com/MacroPhilosophy) and [@BenBeyret](https://twitter.com/BenBeyret) on twitter.
......
agent.py deleted 100644 → 0
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class Agent(object):
def __init__(self):
"""
Load your agent here and initialize anything needed
WARNING: any path to files you wish to access on the docker should be ABSOLUTE PATHS
"""
pass
def reset(self, t=250):
"""
Reset is called before each episode begins
Leave blank if nothing needs to happen there
:param t the number of timesteps in the episode
"""
def step(self, obs, reward, done, info):
"""
A single step the agent should take based on the current state of the environment
We will run the Gym environment (AnimalAIEnv) and pass the arguments returned by env.step() to
the agent.
Note that should if you prefer using the BrainInfo object that is usually returned by the Unity
environment, it can be accessed from info['brain_info'].
:param obs: agent's observation of the current environment
:param reward: amount of reward returned after previous action
:param done: whether the episode has ended.
:param info: contains auxiliary diagnostic information, including BrainInfo.
:return: the action to take, a list or size 2
"""
action = [0, 0]
return action
animalai/LICENSE deleted 100644 → 0
View file @ aa371042
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\ No newline at end of file
animalai/README.md deleted 100644 → 0
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# AnimalAI Python API
This package provides the Python API used for training agents for the Animal AI Olympics competition. It is mostly an
extension of [Unity's MLAgents env](https://github.com/Unity-Technologies/ml-agents/tree/master/ml-agents-envs).
It contains two ways of interfacing with the Unity environments:
- `animalai.envs.environment` contains the `UnityEnvironment` which is similar to the one found in `mlagents` but with
a few adaptations to allow for more custom communications between Python and Unity.
- `animalai.envs.gym.environment` contains the `AnimalAIEnv` which provides a gym environment to use directly with
baselines.
For more details and documentation have a look at the [AnimalAI documentation](../documentation)
\ No newline at end of file
animalai/animalai/communicator_objects/__init__.py
View file @ 83479aa8
from .agent_action_proto_pb2 import *
from .agent_info_proto_pb2 import *
from .arena_parameters_proto_pb2 import *
from .brain_parameters_proto_pb2 import *
from .command_proto_pb2 import *
from .demonstration_meta_proto_pb2 import *
from .engine_configuration_proto_pb2 import *
from .header_pb2 import *
from .arenas_configurations_proto_pb2 import *
from .arena_configuration_proto_pb2 import *
from .items_to_spawn_proto_pb2 import *
from .vector_proto_pb2 import *
from .__init__ import *
\ No newline at end of file
from .resolution_proto_pb2 import *
from .space_type_proto_pb2 import *
from .unity_input_pb2 import *
from .unity_message_pb2 import *
from .unity_output_pb2 import *
from .unity_rl_initialization_input_pb2 import *
from .unity_rl_initialization_output_pb2 import *
from .unity_rl_input_pb2 import *
from .unity_rl_output_pb2 import *
from .unity_rl_reset_input_pb2 import *
from .unity_rl_reset_output_pb2 import *
from .unity_to_external_pb2_grpc import *
from .unity_to_external_pb2 import *
animalai/animalai/communicator_objects/agent_info_proto_pb2.py deleted 100644 → 0
View file @ aa371042
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: animalai/communicator_objects/agent_info_proto.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='animalai/communicator_objects/agent_info_proto.proto',
package='communicator_objects',
syntax='proto3',
serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
serialized_pb=_b('\n4animalai/communicator_objects/agent_info_proto.proto\x12\x14\x63ommunicator_objects\"\x92\x02\n\x0e\x41gentInfoProto\x12\"\n\x1astacked_vector_observation\x18\x01 \x03(\x02\x12\x1b\n\x13visual_observations\x18\x02 \x03(\x0c\x12\x18\n\x10text_observation\x18\x03 \x01(\t\x12\x1d\n\x15stored_vector_actions\x18\x04 \x03(\x02\x12\x1b\n\x13stored_text_actions\x18\x05 \x01(\t\x12\x10\n\x08memories\x18\x06 \x03(\x02\x12\x0e\n\x06reward\x18\x07 \x01(\x02\x12\x0c\n\x04\x64one\x18\x08 \x01(\x08\x12\x18\n\x10max_step_reached\x18\t \x01(\x08\x12\n\n\x02id\x18\n \x01(\x05\x12\x13\n\x0b\x61\x63tion_mask\x18\x0b \x03(\x08\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
)
_AGENTINFOPROTO = _descriptor.Descriptor(
name='AgentInfoProto',
full_name='communicator_objects.AgentInfoProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='stacked_vector_observation', full_name='communicator_objects.AgentInfoProto.stacked_vector_observation', index=0,
number=1, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='visual_observations', full_name='communicator_objects.AgentInfoProto.visual_observations', index=1,
number=2, type=12, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='text_observation', full_name='communicator_objects.AgentInfoProto.text_observation', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='stored_vector_actions', full_name='communicator_objects.AgentInfoProto.stored_vector_actions', index=3,
number=4, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='stored_text_actions', full_name='communicator_objects.AgentInfoProto.stored_text_actions', index=4,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='memories', full_name='communicator_objects.AgentInfoProto.memories', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='reward', full_name='communicator_objects.AgentInfoProto.reward', index=6,
number=7, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='done', full_name='communicator_objects.AgentInfoProto.done', index=7,
number=8, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='max_step_reached', full_name='communicator_objects.AgentInfoProto.max_step_reached', index=8,
number=9, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='id', full_name='communicator_objects.AgentInfoProto.id', index=9,
number=10, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='action_mask', full_name='communicator_objects.AgentInfoProto.action_mask', index=10,
number=11, type=8, cpp_type=7, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=79,
serialized_end=353,
)
DESCRIPTOR.message_types_by_name['AgentInfoProto'] = _AGENTINFOPROTO
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
AgentInfoProto = _reflection.GeneratedProtocolMessageType('AgentInfoProto', (_message.Message,), {
'DESCRIPTOR' : _AGENTINFOPROTO,
'__module__' : 'animalai.communicator_objects.agent_info_proto_pb2'
# @@protoc_insertion_point(class_scope:communicator_objects.AgentInfoProto)
})
_sym_db.RegisterMessage(AgentInfoProto)
DESCRIPTOR._options = None
# @@protoc_insertion_point(module_scope)
animalai/animalai/communicator_objects/agent_action_proto_pb2.py → animalai/animalai/communicator_objects/arena_configuration_proto_pb2.py
View file @ 83479aa8
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: animalai/communicator_objects/agent_action_proto.proto
# source: animalai/communicator_objects/arena_configuration_proto.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
......@@ -8,85 +7,89 @@ from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from animalai.communicator_objects import items_to_spawn_proto_pb2 as animalai_dot_communicator__objects_dot_items__to__spawn__proto__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='animalai/communicator_objects/agent_action_proto.proto',
name='animalai/communicator_objects/arena_configuration_proto.proto',
package='communicator_objects',
syntax='proto3',
serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
serialized_pb=_b('\n6animalai/communicator_objects/agent_action_proto.proto\x12\x14\x63ommunicator_objects\"a\n\x10\x41gentActionProto\x12\x16\n\x0evector_actions\x18\x01 \x03(\x02\x12\x14\n\x0ctext_actions\x18\x02 \x01(\t\x12\x10\n\x08memories\x18\x03 \x03(\x02\x12\r\n\x05value\x18\x04 \x01(\x02\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
)
serialized_pb=_b('\n=animalai/communicator_objects/arena_configuration_proto.proto\x12\x14\x63ommunicator_objects\x1a\x38\x61nimalai/communicator_objects/items_to_spawn_proto.proto\"\x81\x01\n\x17\x41renaConfigurationProto\x12\t\n\x01t\x18\x01 \x01(\x05\x12\x11\n\tpass_mark\x18\x02 \x01(\x02\x12\x35\n\x05items\x18\x03 \x03(\x0b\x32&.communicator_objects.ItemToSpawnProto\x12\x11\n\tblackouts\x18\x04 \x03(\x05\x42\x14\xaa\x02\x11\x41\x41IOCommunicatorsb\x06proto3')
,
dependencies=[animalai_dot_communicator__objects_dot_items__to__spawn__proto__pb2.DESCRIPTOR,])
_AGENTACTIONPROTO = _descriptor.Descriptor(
name='AgentActionProto',
full_name='communicator_objects.AgentActionProto',
_ARENACONFIGURATIONPROTO = _descriptor.Descriptor(
name='ArenaConfigurationProto',
full_name='communicator_objects.ArenaConfigurationProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='vector_actions', full_name='communicator_objects.AgentActionProto.vector_actions', index=0,
number=1, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
name='t', full_name='communicator_objects.ArenaConfigurationProto.t', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='text_actions', full_name='communicator_objects.AgentActionProto.text_actions', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
name='pass_mark', full_name='communicator_objects.ArenaConfigurationProto.pass_mark', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),