def factory(Model, model_args, env_args, optim_args, memory_args, learner_args,
name):
model = Model(**model_args)
env = CartPole(**env_args)
optim = torch.optim.SGD(model.parameters(), **optim_args)
crit = torch.nn.MSELoss()
l_args = dict(learner_args)
l_args['name'] = f"{learner_args['name']}_{name}"
return pg.QLearner(env=env,
model=model,
optimizer=optim,
crit=crit,
action_selector=sp.QActionSelection(temperature=.3),
callbacks=[rcb.MemoryUpdater(1.)],
**l_args)
def factory(Model, model_args, env_args, optim_args, memory_args, learner_args,
crit_args, temp, name):
model = Model(**model_args)
env = TorchGym(env_args['env_name'])
optim = torch.optim.SGD(model.parameters(), **optim_args)
crit = torch.nn.MSELoss(**crit_args)
l_args = dict(learner_args)
l_args['name'] = f"{learner_args['name']}_{name}"
return rl.QLearner(env=env,
model=model,
optimizer=optim,
crit=crit,
action_selector=sp.QActionSelection(temperature=temp),
callbacks=[],
**l_args)
def factory(Model, core, model_args, env_args, optim_args, memory_args,
learner_args, name):
model = Model(core, **model_args)
env = CartPole(**env_args)
memory_updater = MemoryUpdater(**memory_args)
optim = torch.optim.SGD(model.parameters(), **optim_args)
crit = torch.nn.MSELoss()
learner_args['name'] = name
return pg.QLearner(env=env,
model=model,
optimizer=optim,
memory_updater=memory_updater,
crit=crit,
action_selector=sp.QActionSelection(temperature=.3),
callbacks=[
cb.Checkpointer(),
rcb.EnvironmentEvaluator(env=env,
n_evaluations=10,
frequency=1),
],
**learner_args)
def factory(Model, model_args, optim_args, learner_args, crit_args, name, env):
model = Model(**model_args)
# env = TorchGym(**env_args)
optim = torch.optim.SGD(model.parameters(), **optim_args)
crit = torch.nn.MSELoss(**crit_args)
l_args = dict(learner_args)
l_args['name'] = f"{learner_args['name']}_{name}"
return rl.QLearner(
env=env,
model=model,
optimizer=optim,
crit=crit,
action_selector=None,
# memory=Memory(**memory_args),
callbacks=[
# rcb.EnvironmentEvaluator(env=env,
# n_evaluations=10,
# frequency=10,
# action_selector=sp.GreedyValueSelection()),
],
**l_args)
crit = torch.nn.MSELoss()
memory_updater = MemoryUpdater(memory_refresh_rate=.1)
learner = pg.QLearner(env=env,
model=model,
optimizer=optim,
memory_updater=memory_updater,
crit=crit,
action_selector=pg.QActionSelection(temperature=.3),
# action_selector=pg.EpsilonGreedyActionSelection(action_space=np.arange(env.action_space.n),
# epsilon=.95),
gamma=.95,
alpha=.2,
batch_size=256,
n_samples=8000,
grad_clip=5.,
memory_size=10000,
load_checkpoint=False,
name='test_Q',
callbacks=[
rcb.EnvironmentEvaluator(env=env, n_evaluations=10, frequency=5),
# rcb.AgentVisualizer(env=env, frequency=5),
cb.MetricPlotter(frequency=1, metric='rewards', smoothing_window=100),
cb.MetricPlotter(frequency=1, metric='train_losses', smoothing_window=100),
cb.MetricPlotter(frequency=1, metric='avg_reward', smoothing_window=5),
cb.MetricPlotter(frequency=5, metric='val_reward', x='val_epoch', smoothing_window=5),
],
dump_path='tests/q_learner/tmp',
device='cpu')
learner.fit(30, 'cpu', restore_early_stopping=False, verbose=False)