diff --git a/.buildinfo b/.buildinfo index 5622ad99..8f08983b 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 63a8af9b3368f5f776b1597bc8fb05ae +config: d1a85c866a751920eccef77da5d322f7 tags: d77d1c0d9ca2f4c8421862c7c5a0d620 diff --git a/_images/momultiwalker.gif b/_images/momultiwalker.gif deleted file mode 100644 index ae19bab2..00000000 Binary files a/_images/momultiwalker.gif and /dev/null differ diff --git a/_static/scripts/furo-extensions.js b/_static/scripts/furo-extensions.js deleted file mode 100644 index e69de29b..00000000 diff --git a/_static/scripts/furo.js.LICENSE.txt b/_static/scripts/furo.js.LICENSE.txt deleted file mode 100644 index 1632189c..00000000 --- a/_static/scripts/furo.js.LICENSE.txt +++ /dev/null @@ -1,7 +0,0 @@ -/*! - * gumshoejs v5.1.2 (patched by @pradyunsg) - * A simple, framework-agnostic scrollspy script. - * (c) 2019 Chris Ferdinandi - * MIT License - * http://github.com/cferdinandi/gumshoe - */ diff --git a/environments/mobeach_domain/index.html b/environments/mobeach_domain/index.html deleted file mode 100644 index f4a76dba..00000000 --- a/environments/mobeach_domain/index.html +++ /dev/null @@ -1,327 +0,0 @@ - - - - - - - - - MO-BeachDomain - MOMAland Documentation - - - - - - - - - - - - - - - - Contents - - - - - - Menu - - - - - - - - Expand - - - - - - Light mode - - - - - - - - - - - - - - Dark mode - - - - - - - Auto light/dark mode - - - - - - - - - - - - - - - - - - - -
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MO-BeachDomain#

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Agents names

agent_i for i in [0, 99]

Action Space

Discrete(3)

Observation Space

Box(0.0, 100.0, (5,), float32)

Reward Space

Box(0.0, 18.73087, (2,), float32)

Import

momaland.envs.mobeach_domain_v0

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Environment for MO-BeachDomain problem.

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MO-CongestionGame#

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Agents names

agent_i for i in [0, 4199]

Action Space

Discrete(3)

Observation Space

Box(0.0, 4200.0, (1,), float32)

Reward Space

Box(-3.0, 0.0, (2,), float32)

Import

momaland.envs.mocongestion_game_v0

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Environment for MO-CongestionGame problem.

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Mogemmining#

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Agents names

agent_i for i in [0, 19]

Action Space

[‘0: Discrete(4)’, ‘1: Discrete(3, start=1)’, ‘2: Discrete(4, start=2)’, ‘3: Discrete(2, start=3)’, ‘4: Discrete(3, start=4)’, ‘5: Discrete(3, start=5)’, ‘6: Discrete(4, start=6)’, ‘7: Discrete(3, start=7)’, ‘8: Discrete(4, start=8)’, ‘9: Discrete(3, start=9)’, ‘10: Discrete(4, start=10)’, ‘11: Discrete(2, start=11)’, ‘12: Discrete(4, start=12)’, ‘13: Discrete(4, start=13)’, ‘14: Discrete(2, start=14)’, ‘15: Discrete(4, start=15)’, ‘16: Discrete(4, start=16)’, ‘17: Discrete(3, start=17)’, ‘18: Discrete(4, start=18)’, ‘19: Discrete(3, start=19)’]

Observation Space

Box(0.0, 20.0, (1,), float32)

Reward Space

Box(0.0, 23.0, (2,), float32)

Import

momaland.envs.mogemmining_v0

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Environment for MO gem mining domain.

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Moitemgathering#

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Agents names

agent_i for i in [0, 1]

Action Space

Discrete(5)

Observation Space

Tuple(Discrete(2, start=-2), Box(-2, 3, (8, 8), int64))

Reward Space

Box(0.0, 3.0, (3,), float32)

Import

momaland.envs.moitemgathering_v0

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Environment for the Item Gathering domain.

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MO-Multiwalker

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Agents names

walker_i for i in [0, 2]

Action Space

Box(-1.0, 1.0, (4,), float32)

Observation Space

Box(-inf, inf, (31,), float32)

Reward Space

Box([ -0.46666667 -110. -100. ], [0.46666667 0. 0. ], (3,), float32)

Import

momaland.envs.momultiwalker_v0

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An MO adaptation of the multiwalker environment.

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Observation Space

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See PettingZoo documentation.

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Action Space

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The action space is a vector representing the force exerted at the 4 available joints (hips and knees), giving a continuous action space with a 4 element vector. -The higher bound is 1, the lower bound is -1.

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The reward space is a 3D vector containing rewards for:

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The episode is terminated if the package is dropped. If terminate_on_fall is True (default), then environment is terminated if a single agent falls even if the package is still alive.

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Arguments

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See PettingZoo documentation.

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API#

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As for PettingZoo, the MOMAland API models environments as simple Python env classes. Creating environment instances and interacting with them is very simple - here’s an example using the “mosurround_v0” environment:

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-env = momaland.envs.crazyrl.surround.surround_v0.parallel_env()
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-obs, info = env.reset()
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-actions = {agent: env.action_spaces[agent].sample() for agent in env.agents}
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For details on multi-objective multi-agent RL definitions, see Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey.

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You can also check more examples in this colab notebook! TODO

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Training#

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There are learning algorithms present in the codebase for developers to get familiar with MOMA training. Various MOMA algorithms can be accessed here.

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