Chef's Hat Players Club
The Chef's Hat Player club provides ready-to-use agents for the Chef's Hat game.
Instalation
The instalation can be done via pip:
pip install ChefsHatPlayersClub##Available Agents
Agents are divided into types, currently these are the types of available agents. You can check th examples folder for more examples!
Classic Agents
The classic agents are the ones available in our initial investigations with the Chef's Hat:
- Barros, P., Tanevska, A., & Sciutti, A. (2020). Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Game. arXiv preprint arXiv:2004.04000.
Each type of the agent refers on how the agent was trained. Refer to the above paper for more information.
| Agent | Types |
|---|---|
| DQL | "Scratch", "vsRandom", "vsSelf", "vsEveryone" |
| PPO | "Scratch", "vsRandom", "vsSelf", "vsEveryone" |
Each agent can be instantiated using:
agent = AgentDQL(name="DQL4", continueTraining=False, type="vsEveryone", initialEpsilon=1, verbose=True, logDirectory="") # training agentwhere the initialization parameters are:
| Parameter | Description |
|---|---|
| name | name of the agent, must be unique in a game |
| continueTraining | if the agent will learn during the game |
| type | type of the agent, see table above |
| initialEpsilon | when learning, initial exploration value |
| saveFolder | folder that the agent will be saved in |
| verbose | verbose when learning |
| logDirectory | The directory where the agent log will be located |
Karma Chameleon Club
Implementation of an Adversarial Inverse Reinforcement Learning Agent (AIRL), and instances of trained agents based on human data collected from Chef's Hat Online.
| Type | Type | Type | Type | Type |
|---|---|---|---|---|
| lil_abcd_ | lilAbsol | lilAle | lilAna | lilArkady |
| lilAuar | lilBlio1 | lilBlio2 | lilChu | lilDa48 |
| lilDana | lilDJ | lilDomi948 | lilEle | lilFael |
| lilGeo | lilIlzy | lilJba | lilLeandro | lilLena |
| lilLordelo | lilMars | lilNathalia | lilNik | lilNilay |
| lilRamsey | lilRaos | lilThecube | lilThuran | lilTisantana |
| lilToran | lilWinne | lilYves | lilYves2 |
Each agent can be instantiated using:
agent = AgentAIRL(name="AIRL", continueTraining=False, type="lilDJ", initialEpsilon=1, verbose=True, logDirectory="") # training agentwhere the initialization parameters are:
| Parameter | Description |
|---|---|
| name | name of the agent, must be unique in a game |
| continueTraining | if the agent will learn during the game. Must have a demonstration if continue learning is True. |
| demonstrations | must be a npy with a list of ([state, action, possibleActions]) |
| type | type of the agent, see table above |
| initialEpsilon | when learning, initial exploration value |
| saveFolder | folder that the agent will be saved in |
| verbose | verbose when learning |
| logDirectory | The directory where the agent log will be located |
Chef`s Hat Cup 1
Implementations of the agents that won the Chef`s Hat Cup 1. For more information please visit: https://github.com/yvesmendes/ChefsHatPlayersClubAgents/
| Agent | Description |
|---|---|
| AIACIMP | use a custom loss function based on the Hub Loss and a custom reward |
| AINSA | Use a custom reward |
| ALLIN | Custom reward |
| AMYG4 | Only winning reward |
where the initialization parameters are:
| Parameter | Description |
|---|---|
| name | name of the agent, must be unique in a game |
| continueTraining | if the agent will learn during the game. Must have a demonstration if continue learning is True. |
| demonstrations | must be a npy with a list of ([state, action, possibleActions]) |
| initialEpsilon | when learning, initial exploration value |
| saveFolder | folder that the agent will be saved in |
| verbose | verbose when learning |
| logDirectory | The directory where the agent log will be located |
agent = AIACIMP(name="01", continueTraining=False, initialEpsilon=0.2, loadNetwork="", saveFolder="", verbose=True, logDirectory=logDirectory)
agent = AINSA(name="01", continueTraining=False, initialEpsilon=0.2, loadNetwork="", saveFolder="", verbose=True, logDirectory=logDirectory)
agent = ALLIN(name="01", continueTraining=False, initialEpsilon=0.2, loadNetwork="", saveFolder="", verbose=True, logDirectory=logDirectory)
agent = AMYG4(name="01", continueTraining=False, initialEpsilon=0.2, loadNetwork="", saveFolder="", verbose=True, logDirectory=logDirectory)Chef`s Hat Cup 2
Implementations of the agents that won the Chef`s Hat Cup 2.
| Agent | Description |
|---|---|
| Larger_Value | Discard the larger amount of high cards, avoiding to pass |
| BOOM | Discard the larger amount of high cards |
| PPO_V2 | PPO algorithm trained with a custom reward based on finishing position |
agent = Bloom(name="001", saveModelIn="", verbose=True, savelogDirectory=logDirectory)
agent = AgentLargerValue(name="002")
agent = PPO_V2(name="003"continueTraining=False, initialEpsilon=1, loadNetwork="", saveFolder="", verbose=False, logDirectory=logDirectory)Citations
-
Barros, P., Yalçın, Ö. N., Tanevska, A., & Sciutti, A. (2023). Incorporating rivalry in reinforcement learning for a competitive game. Neural Computing and Applications, 35(23), 16739-16752.
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Barros, P., & Sciutti, A. (2022). All by Myself: Learning individualized competitive behavior with a contrastive reinforcement learning optimization. Neural Networks, 150, 364-376.
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Barros, P., Yalçın, Ö. N., Tanevska, A., & Sciutti, A. (2022). Incorporating Rivalry in reinforcement learning for a competitive game. Neural Computing and Applications, 1-14.
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Barros, P., Tanevska, A., & Sciutti, A. (2021, January). Learning from learners: Adapting reinforcement learning agents to be competitive in a card game. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 2716-2723). IEEE.
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Barros, P., Sciutti, A., Bloem, A. C., Hootsmans, I. M., Opheij, L. M., Toebosch, R. H., & Barakova, E. (2021, March). It's Food Fight! Designing the Chef's Hat Card Game for Affective-Aware HRI. In Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (pp. 524-528).
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Barros, P., Tanevska, A., Cruz, F., & Sciutti, A. (2020, October). Moody Learners-Explaining Competitive Behaviour of Reinforcement Learning Agents. In 2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (pp. 1-8). IEEE.
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Barros, P., Sciutti, A., Bloem, A. C., Hootsmans, I. M., Opheij, L. M., Toebosch, R. H., & Barakova, E. (2021, March). It's food fight! Designing the chef's hat card game for affective-aware HRI. In Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (pp. 524-528).
Contact
Pablo Barros - pablovin@gmail.com
Alexandre Rodolfo - armp@ecomp.poli.br
