parameters: (self)
docstring: Constructor for the tower environment
docstring_summary: Constructor for the tower environment
docstring_tokens: Constructor for the tower environment
function: def __init__(self):
          """
          Constructor for the tower environment
          """
          from module import getMod
  
          Environment.__init__(self)
          self.problem = None#TowerofHanoi.generate(ROWS, COLS, GRID_DX, GRID_DY)
          self.rewards = TowerRewardStructure()
          self.states = {}
          self.block_states = {}
          action_info = FeatureVectorInfo()
          observation_info = FeatureVectorInfo()
          reward_info = FeatureVectorInfo()
          action_info.add_discrete(0, 5) # select from the moves we can make
          observation_info.add_discrete(0, ROWS-1)
          observation_info.add_discrete(0, COLS-1)
          observation_info.add_discrete(0,360)
          observation_info.add_discrete(0,1)
          observation_info.add_discrete(0,1)
          observation_info.add_discrete(0,1)
          observation_info.add_discrete(0,1)
          reward_info.add_continuous(-100,100)
          self.agent_info = AgentInitInfo(observation_info, action_info, reward_info)
          self.max_steps = MAX_STEPS
function_tokens: def __init__ ( self ) : from module import getMod Environment . __init__ ( self ) self . problem = None #TowerofHanoi.generate(ROWS, COLS, GRID_DX, GRID_DY) self . rewards = TowerRewardStructure ( ) self . states = { } self . block_states = { } action_info = FeatureVectorInfo ( ) observation_info = FeatureVectorInfo ( ) reward_info = FeatureVectorInfo ( ) action_info . add_discrete ( 0 , 5 ) # select from the moves we can make observation_info . add_discrete ( 0 , ROWS - 1 ) observation_info . add_discrete ( 0 , COLS - 1 ) observation_info . add_discrete ( 0 , 360 ) observation_info . add_discrete ( 0 , 1 ) observation_info . add_discrete ( 0 , 1 ) observation_info . add_discrete ( 0 , 1 ) observation_info . add_discrete ( 0 , 1 ) reward_info . add_continuous ( - 100 , 100 ) self . agent_info = AgentInitInfo ( observation_info , action_info , reward_info ) self . max_steps = MAX_STEPS