def train(self,
callbacks: Union[List[core.AgentCallback], core.AgentCallback, None] = None,
num_iterations: int = 10,
max_steps_per_episode: int = 1000,
num_episodes_per_eval: int = 10,
train_context: core.TrainContext = None,
default_plots: bool = None):
"""Evaluates the environment using a uniform random policy.
The evaluation is performed in batches of num_episodes_per_eval episodes.
Args:
callbacks: list of callbacks called during training and evaluation
num_iterations: number of times a batch of num_episodes_per_eval episodes is evaluated.
max_steps_per_episode: maximum number of steps per episode
num_episodes_per_eval: number of episodes played to estimate the average return and steps
train_context: training configuration to be used. if set overrides all other training context arguments.
default_plots: if set adds a set of default callbacks (plot.State, plot.Rewards, plot.Loss,...)
Returns:
train_context: the training configuration containing the loss and sum of rewards encountered
during training
"""
if train_context is None:
train_context = core.TrainContext()
train_context.num_iterations = num_iterations
train_context.max_steps_per_episode = max_steps_per_episode
train_context.num_epochs_per_iteration = 0
train_context.num_iterations_between_eval = 1
train_context.num_episodes_per_eval = num_episodes_per_eval
train_context.learning_rate = 1
super().train(train_context=train_context, callbacks=callbacks, default_plots=default_plots)
return train_context
def test_train(self):
model_config = core.ModelConfig(_lineworld_name)
tc = core.TrainContext()
random_agent = tfagents.TfRandomAgent(model_config=model_config)
random_agent.train(train_context=tc,
callbacks=[duration.Fast(),
log.Iteration()])
assert tc.episodes_done_in_iteration == 1
def test_random_train(self):
model_config = core.ModelConfig("CartPole-v0")
tc = core.TrainContext()
randomAgent = tforce.TforceRandomAgent(model_config=model_config)
randomAgent.train(
train_context=tc,
callbacks=[log.Iteration(),
log.Agent(),
duration.Fast()])
def test_random_train(self):
from easyagents.backends import tforce
model_config = core.ModelConfig(_cartpole_name)
tc = core.TrainContext()
tc.num_iterations = 50
random_agent = tforce.TforceRandomAgent(model_config=model_config)
random_agent.train(train_context=tc, callbacks=[log.Iteration(), log.Agent()])
(min_r, avg_r, max_r) = tc.eval_rewards[tc.episodes_done_in_training]
assert avg_r < 50