Coach's modularity makes adding an agent a simple and clean task, that involves the following steps:

  1. Implement your algorithm in a new file under the agents directory. The agent can inherit base classes such as ValueOptimizationAgent or ActorCriticAgent, or the more generic Agent base class.

    • ValueOptimizationAgent, PolicyOptimizationAgent and Agent are abstract classes. learn_from_batch() should be overriden with the desired behavior for the algorithm being implemented. If deciding to inherit from Agent, also choose_action() should be overriden.

      def learn_from_batch(self, batch):
          Given a batch of transitions, calculates their target values and updates the network.
          :param batch: A list of transitions
          :return: The loss of the training
      def choose_action(self, curr_state, phase=RunPhase.TRAIN):
          choose an action to act with in the current episode being played. Different behavior might be exhibited when training
           or testing.
          :param curr_state: the current state to act upon.  
          :param phase: the current phase: training or testing.
          :return: chosen action, some action value describing the action (q-value, probability, etc)
    • Make sure to add your new agent to agents/

  2. Implement your agent's specific network head, if needed, at the implementation for the framework of your choice. For example architectures/neon_components/ The head will inherit the generic base class Head. A new output type should be added to, and a mapping between the new head and output type should be defined in the get_output_head() function at architectures/neon_components/

  3. Define a new configuration class at, which includes the new agent name in the type field, the new output type in the output_types field, and assigning default values to hyperparameters.
  4. (Optional) Define a preset using the new agent type with a given environment, and the hyperparameters that should be used for training on that environment.