def act_inference(self,
logger: logging.Logger,
session,
interface: RNGDiscretePassThroughInterface,
agent_observation_current):
# Predict the action with the model by sampling from its probability distribution
action, _, _ = self._model.sample_action(session, agent_observation_current, interface.get_action_mask(logger, session))
# Return the predicted action
return action
def act_train(self, logger: logging.Logger, session,
interface: RNGDiscretePassThroughInterface,
agent_observation_current):
# If there is no exploration policy just use the model best prediction (a sample from the probability distribution)
# Note: it is still inherently exploring
best_action, self._current_value_estimate = self._model.sample_action(
session, agent_observation_current,
interface.get_action_mask(logger, session))
# Return the exploration action
return best_action
environment_name: str = 'RNGDiscreteBinary'
# Generate RNG discrete environment
sequence_size: int = 50
range_min: int = -128
range_max: int = 127
acceptance_value: float = math.inf
seed_range_min: int = 0
seed_range_max: int = 0
episode_length: int = 100
threshold_value: float = 0.2
environment: RNGDiscreteBinaryEpisodicEnvironment = RNGDiscreteBinaryEpisodicEnvironment(
environment_name, sequence_size, range_min, range_max, seed_range_min,
seed_range_max, acceptance_value, episode_length, threshold_value)
# Define interfaces
interface: RNGDiscretePassThroughInterface = RNGDiscretePassThroughInterface(
environment)
# Define experiments
success_threshold: float = 0.35
experiment: RNGDiscreteEpisodicExperiment = RNGDiscreteEpisodicExperiment(
"experiment", success_threshold, environment, vpg_agent, interface)
# Define experiments data
testing_episodes: int = 100
test_cycles: int = 10
training_episodes: int = 100
validation_episodes: int = 100
max_training_episodes: int = 35000
episode_length_max: int = 100
plot_sample_density: int = 10
# Run experiment
intro: str = "Data:\n" \
"\nVanilla Policy Gradient with GAE buffer" \