def testCreateAgentWithDefaults(self): # Verifies that we can create and train an agent with the default values. agent = rainbow_agent.JaxRainbowAgent(num_actions=4) observation = onp.ones([84, 84, 1]) agent.begin_episode(observation) agent.step(reward=1, observation=observation) agent.end_episode(reward=1)
def create_incoherent_agent(sess,
environment,
agent_name='incoherent_dqn',
summary_writer=None,
debug_mode=False):
"""Creates an incoherent agent.
Args:
sess: TF session, unused since we are in JAX.
environment: A gym environment (e.g. Atari 2600).
agent_name: str, name of the agent to create.
summary_writer: A Tensorflow summary writer to pass to the agent
for in-agent training statistics in Tensorboard.
debug_mode: bool, unused.
Returns:
An active and passive agent.
"""
assert agent_name is not None
del sess
del debug_mode
if agent_name == 'dqn':
return jax_dqn_agent.JaxDQNAgent(
num_actions=environment.action_space.n,
summary_writer=summary_writer)
elif agent_name == 'quantile':
return jax_quantile_agent.JaxQuantileAgent(
num_actions=environment.action_space.n,
summary_writer=summary_writer)
elif agent_name == 'rainbow':
return jax_rainbow_agent.JaxRainbowAgent(
num_actions=environment.action_space.n,
summary_writer=summary_writer)
elif agent_name == 'implicit_quantile':
return jax_implicit_quantile_agent.JaxImplicitQuantileAgent(
num_actions=environment.action_space.n,
summary_writer=summary_writer)
elif agent_name == 'incoherent_dqn':
return incoherent_dqn_agent.IncoherentDQNAgent(
num_actions=environment.action_space.n,
summary_writer=summary_writer)
elif agent_name == 'incoherent_implicit_quantile':
return incoherent_implicit_quantile_agent.IncoherentImplicitQuantileAgent(
num_actions=environment.action_space.n,
summary_writer=summary_writer)
elif agent_name == 'mimplicit_quantile':
return incoherent_implicit_quantile_agent.IncoherentImplicitQuantileAgent(
num_actions=environment.action_space.n,
coherence_weight=0.0,
tau=0.03,
summary_writer=summary_writer)
elif agent_name == 'incoherent_mimplicit_quantile':
return incoherent_implicit_quantile_agent.IncoherentImplicitQuantileAgent(
num_actions=environment.action_space.n,
tau=0.03,
summary_writer=summary_writer)
else:
raise ValueError('Unknown agent: {}'.format(agent_name))
def create_agent(sess,
environment,
agent_name=None,
summary_writer=None,
debug_mode=False):
"""Creates an agent.
Args:
sess: A `tf.compat.v1.Session` object for running associated ops.
environment: A gym environment (e.g. Atari 2600).
agent_name: str, name of the agent to create.
summary_writer: A Tensorflow summary writer to pass to the agent
for in-agent training statistics in Tensorboard.
debug_mode: bool, whether to output Tensorboard summaries. If set to true,
the agent will output in-episode statistics to Tensorboard. Disabled by
default as this results in slower training.
Returns:
agent: An RL agent.
Raises:
ValueError: If `agent_name` is not in supported list.
"""
assert agent_name is not None
if not debug_mode:
summary_writer = None
if agent_name == 'dqn':
return dqn_agent.DQNAgent(sess,
num_actions=environment.action_space.n,
summary_writer=summary_writer)
elif agent_name == 'rainbow':
return rainbow_agent.RainbowAgent(
sess,
num_actions=environment.action_space.n,
summary_writer=summary_writer)
elif agent_name == 'implicit_quantile':
return implicit_quantile_agent.ImplicitQuantileAgent(
sess,
num_actions=environment.action_space.n,
summary_writer=summary_writer)
elif agent_name == 'jax_dqn':
return jax_dqn_agent.JaxDQNAgent(
num_actions=environment.action_space.n,
summary_writer=summary_writer)
elif agent_name == 'jax_quantile':
return jax_quantile_agent.JaxQuantileAgent(
num_actions=environment.action_space.n,
summary_writer=summary_writer)
elif agent_name == 'jax_rainbow':
return jax_rainbow_agent.JaxRainbowAgent(
num_actions=environment.action_space.n,
summary_writer=summary_writer)
elif agent_name == 'jax_implicit_quantile':
return jax_implicit_quantile_agent.JaxImplicitQuantileAgent(
num_actions=environment.action_space.n,
summary_writer=summary_writer)
else:
raise ValueError('Unknown agent: {}'.format(agent_name))
def testStoreTransitionWithPrioritizedSamplingy(self):
agent = rainbow_agent.JaxRainbowAgent(
num_actions=4, replay_scheme='prioritized')
dummy_frame = onp.zeros((84, 84))
# Adding transitions with default, 10., default priorities.
agent._store_transition(dummy_frame, 0, 0, False)
agent._store_transition(dummy_frame, 0, 0, False, priority=10.)
agent._store_transition(dummy_frame, 0, 0, False)
returned_priorities = agent._replay.get_priority(
onp.arange(self.stack_size - 1, self.stack_size + 2, dtype=onp.int32))
expected_priorities = [1., 10., 10.]
onp.array_equal(returned_priorities, expected_priorities)
def _create_test_agent(self):
# This dummy network allows us to deterministically anticipate that
# action 0 will be selected by an argmax.
# In Rainbow we are dealing with a distribution over Q-values,
# which are represented as num_atoms bins, ranging from -vmax to vmax.
# The output layer will have num_actions * num_atoms elements,
# so each group of num_atoms weights represent the logits for a
# particular action. By setting 1s everywhere, except for the first
# num_atoms (representing the logits for the first action), which are
# set to onp.arange(num_atoms), we are ensuring that the first action
# places higher weight on higher Q-values; this results in the first
# action being chosen.
class MockRainbowNetwork(linen.Module):
"""Custom Jax network used in tests."""
num_actions: int
num_atoms: int
inputs_preprocessed: bool = False
@linen.compact
def __call__(self, x, support):
def custom_init(key, shape, dtype=jnp.float32):
del key
to_pick_first_action = onp.ones(shape, dtype)
to_pick_first_action[:, :self.num_atoms] = onp.arange(
1, self.num_atoms + 1)
return to_pick_first_action
x = x.astype(jnp.float32)
x = x.reshape((-1)) # flatten
x = linen.Dense(features=self.num_actions * self.num_atoms,
kernel_init=custom_init,
bias_init=linen.initializers.ones)(x)
logits = x.reshape((self.num_actions, self.num_atoms))
probabilities = linen.softmax(logits)
qs = jnp.sum(support * probabilities, axis=1)
return atari_lib.RainbowNetworkType(qs, logits, probabilities)
agent = rainbow_agent.JaxRainbowAgent(
network=MockRainbowNetwork,
num_actions=self._num_actions,
num_atoms=self._num_atoms,
vmax=self._vmax,
min_replay_history=self._min_replay_history,
epsilon_fn=lambda w, x, y, z: 0.0, # No exploration.
epsilon_eval=0.0,
epsilon_decay_period=self._epsilon_decay_period)
# This ensures non-random action choices (since epsilon_eval = 0.0) and
# skips the train_step.
agent.eval_mode = True
return agent
