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Diffstat (limited to 'test/files/neg/t6829.scala')
-rw-r--r-- | test/files/neg/t6829.scala | 64 |
1 files changed, 64 insertions, 0 deletions
diff --git a/test/files/neg/t6829.scala b/test/files/neg/t6829.scala new file mode 100644 index 0000000000..7cbe3c9542 --- /dev/null +++ b/test/files/neg/t6829.scala @@ -0,0 +1,64 @@ +package bugs + +/** + * Created with IntelliJ IDEA. + * User: arya + * Date: 12/18/12 + * Time: 4:17 PM + * To change this template use File | Settings | File Templates. + */ +object currenttype2 { + + type Reward = Double + + trait AbstractAgent[State,Action] { + type A = AbstractAgent[State,Action] + def chooseAction(s: State): Action + def startEpisode: A = this + def learn(s1: State, a: Action, s2: State, r: Reward): A + } + + case class RewardFunction[State,Action](r: (State,Action,State) => Reward) + + trait Rules[G<:GameDomain] { + def simulate(state: G#State, agentActions: List[(G#Agent,G#Action)]): G#State + } + + trait AgentSimulation[G<:GameDomain] { + val agents: List[G#Agent] + val state: G#State + val rewards: Map[G#Agent,G#Rewards] + val rules: Rules[G] + val pastHistory: List[G#State] + lazy val currentHistory = state :: pastHistory + + lazy val actions: Map[G#Agent,G#Action] = agents.map(a => a -> a.chooseAction(state)).toMap + lazy val nextState: G#State = rules.simulate(state, actions.toList) + + def step: AgentSimulation[G] + } + + case class LearningSimulation[G<:GameDomain](agents: List[G#Agent], + state: G#State, + rewards: Map[G#Agent,G#Rewards], + rules: Rules[G], + pastHistory: List[G#State] = Nil) extends AgentSimulation + { + lazy val step: LearningSimulation = { + val updatedAgents: List[G#Agent] = agents map { agent => + val (s,a,s2) = (state,actions(agent),nextState) + val r = rewards(agent).r(s,a,s2) + agent.learn(s,a,s2,r): G#Agent + } + copy(agents = updatedAgents, state = nextState, pastHistory = currentHistory) + } + } + + trait GameDomain { + domain => + type State + type Action + type Agent = AbstractAgent[State, Action] // agent supertype + type Rewards = RewardFunction[State,Action] + } + } |