def test_train(self):
model_config = core.ModelConfig(_lineworld_name)
tc = core.PpoTrainContext()
ppo_agent = tfagents.TfPpoAgent(model_config=model_config)
ppo_agent.train(train_context=tc,
callbacks=[duration.Fast(),
log.Iteration()])
def test_ppo_train(self):
model_config = core.ModelConfig("CartPole-v0")
tc = core.PpoTrainContext()
ppoAgent = tforce.TforcePpoAgent(model_config=model_config)
ppoAgent.train(
train_context=tc,
callbacks=[log.Iteration(),
log.Agent(),
duration.Fast()])
def test_ppo_train(self):
from easyagents.backends import tforce
model_config = core.ModelConfig(_cartpole_name)
tc = core.PpoTrainContext()
tc.num_iterations = 20
ppo_agent = tforce.TforcePpoAgent(model_config=model_config)
ppo_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 > 100
def test_train_cartpole(self):
for backend in get_backends(PpoAgent):
ppo = PpoAgent(gym_env_name="CartPole-v0", backend=backend)
tc = core.PpoTrainContext()
tc.num_iterations = 3
tc.num_episodes_per_iteration = 10
tc.max_steps_per_episode = 500
tc.num_epochs_per_iteration = 5
tc.num_iterations_between_eval = 2
tc.num_episodes_per_eval = 5
ppo.train([log.Iteration()], train_context=tc)
def test_train(self):
agents.seed = 0
for backend in get_backends(PpoAgent):
ppo = PpoAgent(gym_env_name=_cartpole_name, backend=backend)
tc = core.PpoTrainContext()
tc.num_iterations = 10
tc.num_episodes_per_iteration = 10
tc.max_steps_per_episode = 200
tc.num_epochs_per_iteration = 5
tc.num_iterations_between_eval = 5
tc.num_episodes_per_eval = 5
ppo.train([log.Iteration()], train_context=tc, default_plots=False)
assert max_avg_rewards(tc) >= 50
def test_save_(self):
from easyagents.backends import tforce
model_config = core.ModelConfig(_cartpole_name)
tc = core.PpoTrainContext()
tc.num_iterations = 3
ppo_agent = tforce.TforcePpoAgent(model_config=model_config)
ppo_agent.train(train_context=tc, callbacks=[log.Iteration(), log.Agent()])
tempdir = bcore._get_temp_path()
bcore._mkdir(tempdir)
ppo_agent.save(tempdir, [])
loaded_agent = tforce.TforcePpoAgent(model_config=model_config)
loaded_agent.load(tempdir, [])
bcore._rmpath(tempdir)
def train(self,
callbacks: Union[List[core.AgentCallback], core.AgentCallback,
None] = None,
num_iterations: int = 100,
num_episodes_per_iteration: int = 10,
max_steps_per_episode: int = 500,
num_epochs_per_iteration: int = 10,
num_iterations_between_eval: int = 5,
num_episodes_per_eval: int = 10,
learning_rate: float = 0.001,
train_context: core.PpoTrainContext = None,
default_plots: bool = None):
"""Trains a new model using the gym environment passed during instantiation.
Args:
callbacks: list of callbacks called during training and evaluation
num_iterations: number of times the training is repeated (with additional data)
num_episodes_per_iteration: number of episodes played per training iteration
max_steps_per_episode: maximum number of steps per episode
num_epochs_per_iteration: number of times the data collected for the current iteration
is used to retrain the current policy
num_iterations_between_eval: number of training iterations before the current policy is evaluated.
if 0 no evaluation is performed.
num_episodes_per_eval: number of episodes played to estimate the average return and steps
learning_rate: the learning rate used in the next iteration's policy training (0,1]
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,...).
if None default callbacks are only added if the callbacks list is empty
Returns:
train_context: the training configuration containing the loss and sum of rewards encountered
during training
"""
if train_context is None:
train_context = core.PpoTrainContext()
train_context.num_iterations = num_iterations
train_context.num_episodes_per_iteration = num_episodes_per_iteration
train_context.max_steps_per_episode = max_steps_per_episode
train_context.num_epochs_per_iteration = num_epochs_per_iteration
train_context.num_iterations_between_eval = num_iterations_between_eval
train_context.num_episodes_per_eval = num_episodes_per_eval
train_context.learning_rate = learning_rate
super().train(train_context=train_context,
callbacks=callbacks,
default_plots=default_plots)
return train_context
def test_save_load(self):
model_config = core.ModelConfig(_lineworld_name)
tc = core.PpoTrainContext()
ppo_agent = tfagents.TfPpoAgent(model_config=model_config)
ppo_agent.train(
train_context=tc,
callbacks=[duration._SingleIteration(),
log.Iteration()])
tempdir = bcore._get_temp_path()
bcore._mkdir(tempdir)
ppo_agent.save(tempdir, [])
ppo_agent = tfagents.TfPpoAgent(model_config=model_config)
ppo_agent.load(tempdir, [])
pc = core.PlayContext()
pc.max_steps_per_episode = 10
pc.num_episodes = 1
ppo_agent.play(play_context=pc, callbacks=[])
bcore._rmpath(tempdir)
