- ja - Variable in class burlap.behavior.stochasticgame.mavaluefunction.JAQValue
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- ja - Variable in class burlap.behavior.stochasticgame.mavaluefunction.MAValueFunctionPlanner.JointActionTransitions
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- jam - Variable in class burlap.oomdp.stochasticgames.explorers.SGTerminalExplorer
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- jam - Variable in class burlap.oomdp.stochasticgames.SGDomain
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The joint action model of the domain
- JAQValue - Class in burlap.behavior.stochasticgame.mavaluefunction
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Class for storing Q-value informartion for a joint action.
- JAQValue(State, JointAction, double) - Constructor for class burlap.behavior.stochasticgame.mavaluefunction.JAQValue
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- JointAction - Class in burlap.oomdp.stochasticgames
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This class specifies which action each agent took in a world.
- JointAction() - Constructor for class burlap.oomdp.stochasticgames.JointAction
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- JointAction(List<GroundedSingleAction>) - Constructor for class burlap.oomdp.stochasticgames.JointAction
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- jointActionModel - Variable in class burlap.behavior.stochasticgame.agents.mavf.MAVFPlannerFactory.MAVIPlannerFactory
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The joint action model to use in planning.
- jointActionModel - Variable in class burlap.behavior.stochasticgame.mavaluefunction.MAValueFunctionPlanner
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The joint action model to use in planning.
- JointActionModel - Class in burlap.oomdp.stochasticgames
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This abstract class provides the interface and comment mechanisms for defining
a stochastic game's transition dynamics.
- JointActionModel() - Constructor for class burlap.oomdp.stochasticgames.JointActionModel
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- jointActionProbabilities(double[], double[]) - Static method in class burlap.behavior.stochasticgame.solvers.GeneralBimatrixSolverTools
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Computes the joint action probabilities accroding to each player's strategy and returns it as a matrix.
- jointActions - Variable in class burlap.behavior.stochasticgame.GameAnalysis
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The sequence of joint actions
- jointIndex(int, int, int) - Static method in class burlap.behavior.stochasticgame.solvers.CorrelatedEquilibriumSolver
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Returns the 1D array index for a given row and column of a matrix with the given number of columns.
- JointPolicy - Class in burlap.behavior.stochasticgame
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An abstract Policy object for defining a stochastic games joint policy; that is, a policy over joint actions taken by all agents in the world.
- JointPolicy() - Constructor for class burlap.behavior.stochasticgame.JointPolicy
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- jointPolicy - Variable in class burlap.behavior.stochasticgame.PolicyFromJointPolicy
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The underlying joint policy from which actions are selected.
- jointReward - Variable in class burlap.behavior.stochasticgame.agents.mavf.MAVFPlannerFactory.MAVIPlannerFactory
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The joint reward function
- jointReward - Variable in class burlap.behavior.stochasticgame.mavaluefunction.MAValueFunctionPlanner
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The joint reward function
- JointReward - Interface in burlap.oomdp.stochasticgames
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This interface defines the method needed to return the reward received by each agent.
- jointRewardModel - Variable in class burlap.oomdp.stochasticgames.tournament.common.ConstantWorldGenerator
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- jointRewardModel - Variable in class burlap.oomdp.stochasticgames.World
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- jointRewards - Variable in class burlap.behavior.stochasticgame.GameAnalysis
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The sequence of joint rewards
- joinWorld(World, AgentType) - Method in class burlap.behavior.stochasticgame.agents.maql.MultiAgentQLearning
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- joinWorld(World, AgentType) - Method in class burlap.behavior.stochasticgame.agents.mavf.MultiAgentVFPlanningAgent
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- joinWorld(World, AgentType) - Method in class burlap.oomdp.stochasticgames.Agent
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Causes this agent instance to join a world.
- jrs - Variable in class burlap.behavior.stochasticgame.mavaluefunction.MAValueFunctionPlanner.JointActionTransitions
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- JSONPreparedToState(List<Map<String, Object>>) - Method in class burlap.oomdp.auxiliary.common.StateJSONParser
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Takes a JSON prepared datastructure representation of a state and turns it into an actual state object.
- justActionString() - Method in class burlap.oomdp.stochasticgames.GroundedSingleAction
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Returns a string specifying the action name and parameters used in this GroundedSingleAction.