FoPra Beluga Challenge - Reinforcement Learning v1.0
Deep Reinforcement Learning solution for the Beluga Challenge shipping container optimization problem using PPO and MCTS
rl.env.state.Jig Class Reference

Individual jig instance with type and status. More...

Public Member Functions

 __init__ (self, JigType jig_type, bool empty)
 Initialize a jig instance.
 
 __str__ (self)
 
 copy (self)
 Create a deep copy of this jig.
 

Public Attributes

 jig_type = jig_type
 
 empty = empty
 

Detailed Description

Individual jig instance with type and status.

A jig can be empty or loaded, which affects its size and determines valid operations that can be performed on it.

Constructor & Destructor Documentation

◆ __init__()

rl.env.state.Jig.__init__ ( self,
JigType jig_type,
bool empty )

Initialize a jig instance.

Parameters
jig_typeType of this jig (determines size properties)
emptyWhether the jig is currently empty or loaded
68 def __init__(self, jig_type: JigType, empty: bool):
69 """!
70 @brief Initialize a jig instance
71 @param jig_type Type of this jig (determines size properties)
72 @param empty Whether the jig is currently empty or loaded
73 """
74 self.jig_type = jig_type
75 self.empty = empty
76

Member Function Documentation

◆ __str__()

rl.env.state.Jig.__str__ ( self)
77 def __str__(self):
78 return str(self.jig_type) + " | " + str(self.empty)
79

◆ copy()

rl.env.state.Jig.copy ( self)

Create a deep copy of this jig.

Returns
New Jig instance with same properties
80 def copy(self):
81 """!
82 @brief Create a deep copy of this jig
83 @return New Jig instance with same properties
84 """
85 return Jig(self.jig_type, self.empty)
86
87

Member Data Documentation

◆ empty

rl.env.state.Jig.empty = empty

◆ jig_type

rl.env.state.Jig.jig_type = jig_type

The documentation for this class was generated from the following file: