def test_multidiscrete_identity(alg):
'''
Test if the algorithm (with an mlp policy)
can learn an identity transformation (i.e. return observation as an action)
'''
kwargs = learn_kwargs[alg]
kwargs.update(common_kwargs)
learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs)
env_fn = lambda: MultiDiscreteIdentityEnv((3, 3), episode_len=100)
simple_test(env_fn, learn_fn, 0.9)
def test_continuous_identity(alg):
'''
Test if the algorithm (with an mlp policy)
can learn an identity transformation (i.e. return observation as an action)
to a required precision
'''
kwargs = learn_kwargs[alg]
kwargs.update(common_kwargs)
learn_fn = lambda e: get_learn_function(alg)(env=e, **kwargs)
env_fn = lambda: BoxIdentityEnv((1, ), episode_len=100)
simple_test(env_fn, learn_fn, -0.1)
def test_mnist(alg):
'''
Test if the algorithm can learn to classify MNIST digits.
Uses CNN policy.
'''
learn_kwargs = learn_args[alg]
learn_kwargs.update(common_kwargs)
learn = get_learn_function(alg)
def learn_fn(e):
return learn(env=e, **learn_kwargs)
def env_fn():
return MnistEnv(episode_len=100)
simple_test(env_fn, learn_fn, 0.6)
def test_fixed_sequence(alg, rnn):
'''
Test if the algorithm (with a given policy)
can learn an identity transformation (i.e. return observation as an action)
'''
kwargs = learn_kwargs[alg]
kwargs.update(common_kwargs)
if alg == 'ppo2' and rnn.endswith('lstm'):
rnn = 'ppo_' + rnn
def env_fn():
return FixedSequenceEnv(n_actions=10, episode_len=5)
def learn(e):
return get_learn_function(alg)(env=e, network=rnn, **kwargs)
simple_test(env_fn, learn, 0.7)