hive.runners.base module

class hive.runners.base.Runner(environment, agents, logger, experiment_manager, train_steps=1000000, test_frequency=10000, test_episodes=1, max_steps_per_episode=27000)

Bases: ABC

Base Runner class used to implement a training loop.

Different types of training loops can be created by overriding the relevant functions.

Parameters

• environment (BaseEnv) – Environment used in the training loop.

• agents (list[Agent]) – List of agents that interact with the environment.

• logger (ScheduledLogger) – Logger object used to log metrics.

• experiment_manager (Experiment) – Experiment object that saves the state of the training.

• train_steps (int) – How many steps to train for. If this is -1, there is no limit for the number of training steps.

• test_frequency (int) – After how many training steps to run testing episodes. If this is -1, testing is not run.

• test_episodes (int) – How many episodes to run testing for.

train_mode(training)

If training is true, sets all agents to training mode. If training is false, sets all agents to eval mode.

Parameters

training (bool) – Whether to be in training mode.

create_episode_metrics()

Create the metrics used during the loop.

run_one_step(observation, turn, episode_metrics)

Run one step of the training loop.

Parameters

• observation – Current observation that the agent should create an action for.

• turn (int) – Agent whose turn it is.

• episode_metrics (Metrics) – Keeps track of metrics for current episode.

run_end_step(episode_metrics, done)

Run the final step of an episode.

Parameters

• episode_metrics (Metrics) – Keeps track of metrics for current episode.

• done (bool) – Whether this step was terminal.

run_episode()

Run a single episode of the environment.

run_training()

Run the training loop.

run_testing()

Run a testing phase.

resume()

Resume a saved experiment.