4.5 Training

Add text here!

• Single-Agent Scenario Creation

  from mlpro.rl.models import *
  
  class MyScenario(Scenario):
  
      C_NAME      = 'MyScenario'
  
      def _setup(self, p_mode, p_ada:bool, p_logging:bool):
          """
          Here's the place to explicitely setup the entire rl scenario. Please bind your env to
          self._env and your agent to self._agent.
  
          Parameters:
              p_mode              Operation mode of environment (see Environment.C_MODE_*)
              p_ada               Boolean switch for adaptivity of agent
              p_logging           Boolean switch for logging functionality
         """
  
         # Setup environment
         self._env    = MyEnvironment(....)
  
         # Setup an agent with selected policy
         self._agent = Agent(
             p_policy=MyPolicy(
              p_state_space=self._env.get_state_space(),
              p_action_space=self._env.get_action_space(),
              ....
              ),
              ....
          )
  
  # Instantiate scenario
  myscenario  = MyScenario(p_scenario=myscenario, ....)
  
  # Train agent in scenario
  training    = Training(....)
  training.run()
  

• Multi-Agent Scenario Creation

  from mlpro.rl.models import *
  
  class MyScenario(Scenario):
  
      C_NAME      = 'MyScenario'
  
      def _setup(self, p_mode, p_ada:bool, p_logging:bool):
          """
          Here's the place to explicitely setup the entire rl scenario. Please bind your env to
          self._env and your agent to self._agent.
  
          Parameters:
              p_mode              Operation mode of environment (see Environment.C_MODE_*)
              p_ada               Boolean switch for adaptivity of agent
              p_logging           Boolean switch for logging functionality
         """
  
         # Setup environment
         self._env    = MyEnvironment(....)
  
         # Create an empty mult-agent
         self._agent     = MultiAgent(....)
  
         # Add Single-Agent #1 with own policy (controlling sub-environment #1)
         self._agent.add_agent = Agent(
             self._agent = Agent(
                 p_policy=MyPolicy(
                  p_state_space=self._env.get_state_space().spawn[....],
                  p_action_space=self._env.get_action_space().spawn[....],
                  ....
                  ),
                  ....
              ),
              ....
          )
  
         # Add Single-Agent #2 with own policy (controlling sub-environment #2)
         self._agent.add_agent = Agent(....)
  
         ....
  
  # Instantiate scenario
  myscenario  = MyScenario(p_scenario=myscenario, ....)
  
  # Train agent in scenario
  training    = Training(....)
  training.run()