rlgt - This is Reinforcement Learning for Graph Theory (RLGT), a reinforcement learning framework that aims to facilitate future research in extremal graph theory.agents - The rlgt.agents package defines the GraphAgent abstract base class, which formalizes a reinforcement learning agent for graph theory applications. It also provides three concrete agent classes that inherit from ...deep_cross_entropy_agent - This Python module contains the DeepCrossEntropyAgent class, which implements a reinforcement learning agent for graph theory applications using the Deep Cross-Entropy method with PyTorch.graph_agent - This Python module defines the GraphAgent abstract base class, which formalizes the concept of a reinforcement learning agent for graph theory applications.ppo_agent - This Python module contains the PPOAgent class, which implements a reinforcement learning agent for graph theory applications using the Proximal Policy Optimization (PPO) method with PyTorch.random_action_mechanisms - This Python module defines the RandomActionMechanism abstract base class, which formalizes the concept of a random action mechanism in the context of reinforcement learning agents for graph theory applications...reinforce_agent - This Python module contains the ReinforceAgent class, which implements a reinforcement learning agent for graph theory applications using the REINFORCE method with PyTorch.environments - The rlgt.environments package defines the GraphEnvironment abstract base class, which formalizes a reinforcement learning environment for graph theory applications. It also provides seven concrete environment classes that inherit from ...global_environments - This Python module defines two global reinforcement learning environments that inherit from the GraphEnvironment class. These environments model graph building games in which the edges (resp. arcs) are initially fully colored in some predetermined manner, and at each step, any edge (resp...graph_environment - This Python module defines the GraphEnvironment abstract class, which encapsulates the concept of a reinforcement learning (RL) environment for graph theory applications, together with an associated enumeration describing the possible episode statuses.graph_generators - This Python module defines several factory functions for constructing graph generator functions. These generators implement various mechanisms for producing batches of fully colored k-edge-colored looped complete graphs of a specified batch size.linear_environments - This Python module defines three linear reinforcement learning environments that inherit from the GraphEnvironment class and model graph building games where the edges (resp. arcs) are all initially either uncolored, or fully colored in some predetermined manner, and are then properly (re)colored one by one, either in the row-major order or the clockwise order.local_environments - This Python module defines two local reinforcement learning environments that inherit from the GraphEnvironment class and model graph building games in which the edges (resp. arcs) are initially fully colored in some predetermined manner...graphs - The rlgt.graphs package provides the Graph class, which models a k-edge-colored looped complete graph as well as a batch of k-edge-colored looped complete graphs of the same order. The package also provides the ...graph - This Python module contains the Graph class. This class encapsulates the concept of a k-edge-colored looped complete graph or a batch of k-edge-colored looped complete graphs of the same order.graph_formats - This Python module contains several enumerations related to k-edge-colored looped complete graphs:special_graphs - This Python module contains classes that inherit from the Graph class and provide ways to construct k-edge-colored looped complete graphs with specific structures.utils - This Python module provides auxiliary functions for working with k-edge-colored looped complete graphs. The functions include computing flattened lengths, converting between various graph formats, and calculating edge (resp...