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zU}yseR02Q~jVLYM8dM1!1#Hs+PH0(15$L$*uKtlapc2p+6M>F5yn>WHItYf^5LIX61~xKN!a-{J`E#y_-6ayMjLSWhQA#+e8W$`{;qP1oSSUF zgoeGlv!F(3+Q_Esn_8^_nvx0(|vx6OP9uM3@55(D7&+^3lo-iElLW(31Oob7uNx=b?K)nDH_H{ie z0*wuOiWh0{^VW^TuN1vWSD0{a%QO%LZg|oqP2wrUHC)W$^Wnl_BHTA>qDEW!ZE2>H ztk2&i-?Q=;)|w>tAh>`0i`-p|>&mfb&kkpz`=#LXi_0HF}?-` zww>lFig%SQyWH!}8gXOCM~;yz0XKro?|ad{RBK`HEOsp|ZHG_D+~N^y3}MSNys(Cn zT5o(2<~H+o(cDOe8CK#Qav6*H7F$~!)0Pj7M9K!oV%yQB8*gr_T_e9!^+v8bESQ9A zY)_^NTZtngI^S-VP1IS2bnj`<;ZxN2*eqlo4|M0NWuK>86BZGV*uknma z(Jo{o?~$B9f})$>vk!_dSTB}4_rOK$VIqCm&o4CeL{jo*RJ@~q{+bq+sP{2zdga - - - - - -
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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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Episode Termination

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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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import momaland
-import numpy as np
-
-# It follows the original PettingZoo APIs ...
-env = momaland.envs.crazyrl.surround.surround_v0.parallel_env()
-
-obs, info = env.reset()
-# but vector_reward is a numpy array!
-actions = {agent: env.action_spaces[agent].sample() for agent in env.agents}
-next_obs, vector_rewards, terminated, truncated, info = env.step(actions)
-
-# Optionally, you can scalarize the reward function with the LinearReward wrapper to fall back to the original PZ API
-env = momaland.LinearReward(env, weight=np.array([0.6, 0.2, 0.2]))
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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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E1*DV-J^%m! 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 - - - - - - - - - - - - - - -