game: normal_form_extensive_game(game=first_sealed_auction(players=3,max_value=3))

GameType.chance_mode = ChanceMode.DETERMINISTIC
GameType.dynamics = Dynamics.SIMULTANEOUS
GameType.information = Information.ONE_SHOT
GameType.long_name = "Normal-form First-Price Sealed-Bid Auction"
GameType.max_num_players = 3
GameType.min_num_players = 3
GameType.parameter_specification = []
GameType.provides_information_state_string = True
GameType.provides_information_state_tensor = True
GameType.provides_observation_string = False
GameType.provides_observation_tensor = False
GameType.provides_factored_observation_string = False
GameType.reward_model = RewardModel.TERMINAL
GameType.short_name = "normal_form_extensive_game"
GameType.utility = Utility.GENERAL_SUM

NumDistinctActions() = 6
PolicyTensorShape() = [6]
MaxChanceOutcomes() = 0
GetParameters() = {}
NumPlayers() = 3
MinUtility() = 0.074074
MaxUtility() = 1.1111
UtilitySum() = None
InformationStateTensorShape() = [1]
InformationStateTensorLayout() = TensorLayout.CHW
InformationStateTensorSize() = 1
MaxGameLength() = 1
ToString() = "normal_form_extensive_game()"

# State 0
# Terminal? false
IsTerminal() = False
History() = []
HistoryString() = ""
IsChanceNode() = False
IsSimultaneousNode() = True
CurrentPlayer() = -2
InformationStateString(0) = "Observing player: 0. Non-terminal"
InformationStateString(1) = "Observing player: 1. Non-terminal"
InformationStateString(2) = "Observing player: 2. Non-terminal"
InformationStateTensor(0): ◯
InformationStateTensor(1): ◯
InformationStateTensor(2): ◯
Rewards() = [0.0, 0.0, 0.0]
Returns() = [0.0, 0.0, 0.0]
LegalActions(0) = [0, 1, 2, 3, 4, 5]
LegalActions(1) = [0, 1, 2, 3, 4, 5]
LegalActions(2) = [0, 1, 2, 3, 4, 5]
StringLegalActions(0) = ["p0 val 1  ---  action = 0
p0 val 2  ---  action = 0
p0 val 3  ---  action = 0
", "p0 val 1  ---  action = 0
p0 val 2  ---  action = 1
p0 val 3  ---  action = 0
", "p0 val 1  ---  action = 0
p0 val 2  ---  action = 0
p0 val 3  ---  action = 1
", "p0 val 1  ---  action = 0
p0 val 2  ---  action = 1
p0 val 3  ---  action = 1
", "p0 val 1  ---  action = 0
p0 val 2  ---  action = 0
p0 val 3  ---  action = 2
", "p0 val 1  ---  action = 0
p0 val 2  ---  action = 1
p0 val 3  ---  action = 2
"]
StringLegalActions(1) = ["p1 val 1  ---  action = 0
p1 val 2  ---  action = 0
p1 val 3  ---  action = 0
", "p1 val 1  ---  action = 0
p1 val 2  ---  action = 1
p1 val 3  ---  action = 0
", "p1 val 1  ---  action = 0
p1 val 2  ---  action = 0
p1 val 3  ---  action = 1
", "p1 val 1  ---  action = 0
p1 val 2  ---  action = 1
p1 val 3  ---  action = 1
", "p1 val 1  ---  action = 0
p1 val 2  ---  action = 0
p1 val 3  ---  action = 2
", "p1 val 1  ---  action = 0
p1 val 2  ---  action = 1
p1 val 3  ---  action = 2
"]
StringLegalActions(2) = ["p2 val 1  ---  action = 0
p2 val 2  ---  action = 0
p2 val 3  ---  action = 0
", "p2 val 1  ---  action = 0
p2 val 2  ---  action = 1
p2 val 3  ---  action = 0
", "p2 val 1  ---  action = 0
p2 val 2  ---  action = 0
p2 val 3  ---  action = 1
", "p2 val 1  ---  action = 0
p2 val 2  ---  action = 1
p2 val 3  ---  action = 1
", "p2 val 1  ---  action = 0
p2 val 2  ---  action = 0
p2 val 3  ---  action = 2
", "p2 val 1  ---  action = 0
p2 val 2  ---  action = 1
p2 val 3  ---  action = 2
"]

# Apply joint action ["p0 val 1  ---  action = 0
p0 val 2  ---  action = 0
p0 val 3  ---  action = 0
", "p1 val 1  ---  action = 0
p1 val 2  ---  action = 0
p1 val 3  ---  action = 0
", "p2 val 1  ---  action = 0
p2 val 2  ---  action = 0
p2 val 3  ---  action = 1
"]
actions: [0, 0, 2]

# State 1
# Terminal? true
# History: 0, 0, 2
# Returns: 0.444444,0.444444,1
IsTerminal() = True
History() = [0, 0, 2]
HistoryString() = "0, 0, 2"
IsChanceNode() = False
IsSimultaneousNode() = False
CurrentPlayer() = -4
InformationStateString(0) = "Observing player: 0. Terminal. History string: 0, 0, 2"
InformationStateString(1) = "Observing player: 1. Terminal. History string: 0, 0, 2"
InformationStateString(2) = "Observing player: 2. Terminal. History string: 0, 0, 2"
InformationStateTensor(0): ◉
InformationStateTensor(1): ◉
InformationStateTensor(2): ◉
Rewards() = [0.4444444444444444, 0.4444444444444444, 1.0]
Returns() = [0.4444444444444444, 0.4444444444444444, 1.0]
