def test_rl_env_from_snc_env_action_space_dims_simple():
"""
Tests the formation and stability of the action space dimensions of the environment through
the RL environment pipeline in a simple setting.
"""
# Set up the environment parameters.
cost_per_buffer = np.ones((1, 1))
initial_state = (0,)
capacity = np.ones((1, 1)) * np.inf
demand_rate_val = 0.7
job_conservation_flag = True
seed = 72
demand_rate = np.array([demand_rate_val])[:, None]
buffer_processing_matrix = - np.ones((1, 1))
constituency_matrix = np.ones((1, 1))
list_boundary_constraint_matrices = [constituency_matrix]
# Construct environment.
job_generator = ScaledBernoulliServicesPoissonArrivalsGenerator(
demand_rate, buffer_processing_matrix, job_gen_seed=seed)
assert job_generator.routes == {}
state_initialiser = stinit.DeterministicCRWStateInitialiser(initial_state)
env = RLControlledRandomWalk(cost_per_buffer, capacity, constituency_matrix, job_generator,
state_initialiser, job_conservation_flag,
list_boundary_constraint_matrices)
_, action_space_dims = rl_env_from_snc_env(env, discount_factor=0.99, for_tf_agent=False)
_, action_space_dims_tf = rl_env_from_snc_env(env, discount_factor=0.99, for_tf_agent=True)
assert action_space_dims == action_space_dims_tf
assert len(env.action_vectors) == sum(action_space_dims)
def test_rl_env_from_snc_env_action_space_dims_multiple_resource_sets():
"""
Tests the formation and stability of the action space dimensions of the environment through
the RL environment pipeline in a more complex setting.
"""
# Set the environment name for this case as the asserts are difficult to make as variables.
env_name = 'double_reentrant_line_shared_res_homogeneous_cost'
# Set up the environment parameters.
# Environment parameters do not affect the test result here.
env = load_scenario(env_name, job_gen_seed=10).env
rl_env, action_space_dims = rl_env_from_snc_env(env, discount_factor=0.99, for_tf_agent=False)
_, action_space_dims_tf = rl_env_from_snc_env(env, discount_factor=0.99, for_tf_agent=True)
assert action_space_dims == action_space_dims_tf
assert len(rl_env.action_vectors) == sum(action_space_dims)
def test_rl_simulation_agent_serialisation():
"""
Test the custom serialisation of the agent used when saving the state of the SNC simulator.
The customised serialisation was required due to the inability to serialise TensorFlow objects.
"""
# Set up the agent as before.
seed = 72
env = load_scenario("single_server_queue", job_gen_seed=seed).env
rl_env, _ = rl_env_from_snc_env(env, discount_factor=0.99)
rl_agent = create_reinforce_agent(rl_env)
rl_agent.initialize()
del rl_env
sim_agent = RLSimulationAgent(env, rl_agent, normalise_obs=True)
# Attain the dictionary representation of the agent and test that all the attributes expected
# are present.
serialised_agent = sim_agent.to_serializable()
assert all(attr in serialised_agent for attr in [
"_rl_env", "_rl_agent", "_policy", "_is_eval_policy", "env",
"buffer_processing_matrix", "constituency_matrix", "demand_rate",
"list_boundary_constraint_matrices", "name"
])
# Ensure that the dictionary representation is compatible with the json module and the chosen
# encoder.
json_string = json.dumps(serialised_agent,
cls=NumpyEncoder,
indent=4,
sort_keys=True)
assert bool(json_string)
def get_environment(env_name: str,
agent_name: str,
episode_len_to_min_drain_time_ratio: float,
terminal_discount_factor: float = 0.7,
action_repetitions: int = 1,
parallel_environments: int = 8,
env_overload_params: Optional[Dict] = None,
agent_params: Optional[Dict] = None,
seed: Optional[int] = None) \
-> Tuple[TFPyEnvironment, float, float, int, Tuple[int, ...]]:
"""
Builds and initialises a TensorFlow environment implementation of the Single Server Queue.
:param env_name: The name of the scenario to load. Must be in the list of implemented scenarios.
:param agent_name: The name of the RL agent the environment is to be set up for.
:param episode_len_to_min_drain_time_ratio: Maximum number of time steps per episode as a
proportion of the minimal draining time.
:param terminal_discount_factor: The discount applied to the final time step from which a
per-step discount factor is calculated.
:param action_repetitions: Number of time steps each selected action is repeated for.
:param parallel_environments: Number of environments to run in parallel.
:param env_overload_params: Dictionary of parameters to override the scenario defaults.
:param agent_params: Optional dictionary of agent parameters the environment can be adapted for.
:param seed: Random seed used to initialise the environment.
:return: The environment wrapped and ready for TensorFlow Agents.
"""
# Handle some default argument clean up.
if env_overload_params is None:
env_overload_params = {}
env = scenarios.load_scenario(env_name, seed, env_overload_params).env
if np.all(env.state_initialiser.initial_state == 0):
env.max_episode_length = 450
else:
if env.state_initialiser.initial_state.ndim == 1:
initial_state = env.state_initialiser.initial_state.reshape((-1, 1))
else:
initial_state = env.state_initialiser.initial_state
minimal_draining_time = compute_minimal_draining_time_from_env_cvxpy(initial_state, env)
env.max_episode_length = int(episode_len_to_min_drain_time_ratio * minimal_draining_time)
discount_factor = np.exp(np.log(terminal_discount_factor) / env.max_episode_length)
load = np.max(compute_load_workload_matrix(env).load)
max_ep_len = env.max_episode_length
# Allow toggling of observation normalisation in the environment.
# The typical behaviour for PPO is that PPO normalises observations internally as necessary so
# normalisation in the environment is not necessary.
if agent_name == 'ppo' and agent_params.get('normalize_observations', True):
normalise_obs_in_env = False
else:
normalise_obs_in_env = True
# Wrap and parallelise environment for tf agents.
tf_env, action_dims = rl_env_from_snc_env(env,
discount_factor,
action_repetitions,
parallel_environments,
normalise_observations=normalise_obs_in_env)
return tf_env, discount_factor, load, max_ep_len, action_dims
def test_bellman_pets_agent_init_with_multiple_resource_sets():
"""
Tests agent set up and initialisation with multiple action subspaces (multiple resource sets).
"""
# Set the environment name for this case as the asserts are difficult to make as variables.
env_name = 'double_reentrant_line_shared_res_homogeneous_cost'
# Set up the environment parameters.
# Environment parameters do not affect the test result here.
tf_env, _ = rl_env_from_snc_env(load_scenario(env_name,
job_gen_seed=10)[1],
discount_factor=0.99,
normalise_observations=False)
# Instantiate and initialise a PPO agent for the environment.
bellman_pets_agent = create_bellman_pets_agent(
env=tf_env,
reward_model_class=CRWRewardModel,
initial_state_distribution_model_class=CRWInitialStateModel,
)
# Validate initialisation by checking some properties of the initalised agent.
assert isinstance(bellman_pets_agent.action_spec, tuple)
assert len(bellman_pets_agent.action_spec) == 2
assert isinstance(bellman_pets_agent.action_spec[0], BoundedTensorSpec)
assert isinstance(bellman_pets_agent.action_spec[1], BoundedTensorSpec)
assert bellman_pets_agent.action_spec[0].shape == tf.TensorShape((1, 3))
assert bellman_pets_agent.action_spec[1].shape == tf.TensorShape((1, 3))
assert bellman_pets_agent.name == "PETS_Agent"
assert bellman_pets_agent.time_step_spec == tf_env.time_step_spec()
def test_ppo_agent_play(env_name):
"""
Extension of the agent set up and initialisation test to include playing episodes.
"""
# Set up environment using default parameters.
# Environment parameters do not affect the test result here.
tf_env, action_dims = rl_env_from_snc_env(load_scenario(
env_name, job_gen_seed=10)[1],
discount_factor=0.99,
normalise_observations=False)
# Instantiate and initialise a PPO agent for the environment.
ppo_agent = create_ppo_agent(tf_env, num_epochs=10)
ppo_agent.initialize()
# Reset the environment
tf_env.reset()
# Play 5 time steps in the environment.
for _ in range(5):
# Since we do not have the state stored at this point we capture it from the environment
# fresh each time step as a TimeStep object (a named tuple).
time_step = tf_env.current_time_step()
# Attain our agent's action.
action_step = ppo_agent.collect_policy.action(time_step)
# Ensure that the action is one-hot as expected
if isinstance(action_step.action, tuple):
action = tf.concat(action_step.action, axis=-1)
else:
action = action_step.action
# Ensure that the action is binary as expected.
assert snc.is_binary(action)
# Play the action out in the environment.
tf_env.step(action_step.action)
def test_reinforce_agent_learning(env_name):
"""
Extension of the test for an agent playing in the environment to include training.
Note: This does not test that training improves the policy. It simply tests that the training
loop runs effectively.
"""
# Set up environment using default parameters.
# Environment parameters do not affect the test result here.
tf_env, _ = rl_env_from_snc_env(load_scenario(
env_name,
job_gen_seed=10,
override_env_params={'max_episode_length': 25})[1],
discount_factor=0.99)
# Set up a training step counter.
global_step = tf.compat.v1.train.get_or_create_global_step()
# Instantiate a REINFORCE agent
reinforce_agent = create_reinforce_agent(tf_env,
training_step_counter=global_step)
# Instantiate a replay buffer.
replay_buffer = TFUniformReplayBuffer(
data_spec=reinforce_agent.collect_data_spec,
batch_size=tf_env.batch_size,
max_length=1000)
# Initialise the action network weights etc.
reinforce_agent.initialize()
# Use a driver to handle data collection for the agent. This handles a lot of the backend
# TensorFlow set up and solves previous errors with episodes of differing lengths.
collect_driver = DynamicEpisodeDriver(tf_env,
reinforce_agent.collect_policy,
observers=[replay_buffer.add_batch],
num_episodes=2)
# Get the initial states of the agent and environment before training.
time_step = tf_env.reset()
policy_state = reinforce_agent.collect_policy.get_initial_state(
tf_env.batch_size)
# Take a copy of the variables in order to ensure that training does lead to parameter changes.
initial_vars = deepcopy(reinforce_agent.trainable_variables)
assert len(initial_vars) > 0, "Agent has no trainable variables."
# Set up a minimal training loop to simply test training mechanics work.
for _ in range(5):
# Collect experience.
time_step, policy_state = collect_driver.run(time_step=time_step,
policy_state=policy_state)
# Now the replay buffer should have data in it so we can collect the data and train the
# agent.
experience = replay_buffer.gather_all()
reinforce_agent.train(experience)
# Clear the replay buffer and return to play.
replay_buffer.clear()
# Check that training has had some effect
for v1, v2 in zip(initial_vars, reinforce_agent.trainable_variables):
assert not np.allclose(v1.numpy(), v2.numpy())
def test_reinforce_agent_init_with_multiple_resource_sets():
"""
Tests agent set up and initialisation with multiple action subspaces (multiple resource sets).
"""
# Set the environment name for this case as the asserts are difficult to make as variables.
env_name = 'double_reentrant_line_shared_res_homogeneous_cost'
# Set up environment using default parameters.
# Environment parameters do not affect the test result here.
tf_env, _ = rl_env_from_snc_env(load_scenario(
env_name,
job_gen_seed=10,
override_env_params={'max_episode_length': 25})[1],
discount_factor=0.99)
# Instantiate and initialise a REINFORCE agent for the environment.
reinforce_agent = create_reinforce_agent(tf_env)
reinforce_agent.initialize()
# Validate initialisation by checking some properties of the initalised agent.
assert isinstance(reinforce_agent.action_spec, tuple)
assert len(reinforce_agent.action_spec) == 2
assert isinstance(reinforce_agent.action_spec[0], BoundedTensorSpec)
assert isinstance(reinforce_agent.action_spec[1], BoundedTensorSpec)
assert reinforce_agent.action_spec[0].shape == tf.TensorShape((1, 3))
assert reinforce_agent.action_spec[1].shape == tf.TensorShape((1, 3))
assert reinforce_agent.name == "reinforce_agent"
assert reinforce_agent.time_step_spec == tf_env.time_step_spec()
def test_ppo_agent_init_with_multiple_resource_sets():
"""
Tests agent set up and initialisation with multiple action subspaces (multiple resource sets).
"""
# Set the environment name for this case as the asserts are difficult to make as variables.
env_name = 'double_reentrant_line_shared_res_homogeneous_cost'
# Set up the environment parameters.
# Environment parameters do not affect the test result here.
tf_env, _ = rl_env_from_snc_env(load_scenario(env_name,
job_gen_seed=10)[1],
discount_factor=0.99,
normalise_observations=False)
# Instantiate and initialise a PPO agent for the environment.
ppo_agent = create_ppo_agent(tf_env, num_epochs=10)
ppo_agent.initialize()
# Validate initialisation by checking some properties of the initialised agent.
assert isinstance(ppo_agent.action_spec, tuple)
assert len(ppo_agent.action_spec) == 2
assert isinstance(ppo_agent.action_spec[0], BoundedTensorSpec)
assert isinstance(ppo_agent.action_spec[1], BoundedTensorSpec)
assert ppo_agent.action_spec[0].shape == tf.TensorShape((3))
assert ppo_agent.action_spec[1].shape == tf.TensorShape((3))
assert ppo_agent.name == "PPO_Agent"
assert ppo_agent.time_step_spec == tf_env.time_step_spec()
def test_rl_simulation_agent_normalise_obs_usage_with_normalisation():
"""Ensure that the _normalise_obs property of RLSimulationAgent is used correctly."""
# Set up the agent as before.
seed = 72
state = np.array([100, 100, 100, 100])
env = load_scenario("klimov_model",
job_gen_seed=seed,
override_env_params={
"initial_state": state
}).env
rl_env, _ = rl_env_from_snc_env(env,
discount_factor=0.99,
normalise_observations=True)
ppo_agent = MagicMock()
ppo_agent.discount_factor = 0.99
ppo_agent._gamma = 0.99
policy = MagicMock()
ppo_agent.collect_policy = policy
del rl_env
ppo_sim_agent = RLSimulationAgent(env, ppo_agent, normalise_obs=True)
ppo_sim_agent._rl_env.preprocess_action = MagicMock()
ppo_sim_agent.map_state_to_actions(state)
expected_timestep = TimeStep(step_type=StepType(0),
reward=None,
discount=0.99,
observation=state.reshape(1, -1) /
state.sum())
assert policy.action.call_count == 1
call_timestep = policy.action.call_args[0][0]
assert (call_timestep.observation == expected_timestep.observation).all()
def test_reinforce_agent_play(env_name):
"""
Extension of the agent set up and initialisation test to include playing episodes.
"""
# Set up environment using default parameters.
# Environment parameters do not affect the test result here.
tf_env, _ = rl_env_from_snc_env(load_scenario(
env_name,
job_gen_seed=10,
override_env_params={'max_episode_length': 25})[1],
discount_factor=0.99)
# Instantiate and initialise a REINFORCE agent.
reinforce_agent = create_reinforce_agent(tf_env)
reinforce_agent.initialize()
# Reset the environment
tf_env.reset()
# Play 5 time steps in the environment.
for _ in range(5):
# Since we do not have the state stored at this point we capture it from the environment
# fresh each time step as a TimeStep object (a named tuple).
time_step = tf_env.current_time_step()
# Attain our agent's action.
action_step = reinforce_agent.collect_policy.action(time_step)
if isinstance(action_step.action, tuple):
action = tf.concat(action_step.action, axis=-1)
else:
action = action_step.action
# Ensure that the action is binary as expected.
assert snc.is_binary(action)
# Play the action out in the environment.
tf_env.step(action_step.action)
def test_rl_env_normalise_obs_property():
"""
Ensure that the normalise_obs property of RLControlledRandomWalk is set and updated correctly.
"""
# Set the environment name for this case as the asserts are difficult to make as variables.
env_name = 'double_reentrant_line_shared_res_homogeneous_cost'
# Set up the environment parameters.
# Environment parameters do not affect the test result here.
env = load_scenario(env_name, job_gen_seed=10).env
rl_env, _ = rl_env_from_snc_env(env, discount_factor=0.99, for_tf_agent=False)
assert rl_env.normalise_obs is True
rl_env.normalise_obs = False
assert rl_env.normalise_obs is False
def test_rl_simulation_agent_string_representation():
"""
Tests that the string representation of the simulation agent is as expected.
"""
# Set up the agent as before.
seed = 72
env = load_scenario("single_server_queue", job_gen_seed=seed).env
rl_env, _ = rl_env_from_snc_env(env, discount_factor=0.99)
rl_agent = create_reinforce_agent(rl_env)
rl_agent.initialize()
del rl_env
sim_agent = RLSimulationAgent(env, rl_agent, normalise_obs=True)
# Ensure that the string representation of the agent contains the instance name at the end.
assert str(sim_agent)[-len(sim_agent.name):] == sim_agent.name
def test_rl_simulation_agent_discount_factor_ppo():
"""
Tests that the discount factor is passed from a PPO agent to an RLSimulationAgent correctly.
"""
# Set up the agent as before.
seed = 72
env = load_scenario("single_server_queue", job_gen_seed=seed).env
rl_env, _ = rl_env_from_snc_env(env,
discount_factor=0.99,
normalise_observations=False)
ppo_agent = create_ppo_agent(rl_env, gamma=0.90)
ppo_agent.initialize()
del rl_env
ppo_sim_agent = RLSimulationAgent(env, ppo_agent, normalise_obs=False)
assert ppo_sim_agent.discount_factor == 0.90
def test_rl_simulation_agent_normalise_obs_property():
"""Ensure that the _normalise_obs property of RLSimulationAgent is set correctly."""
# Set up the agent as before.
seed = 72
env = load_scenario("single_server_queue", job_gen_seed=seed).env
rl_env, _ = rl_env_from_snc_env(env,
discount_factor=0.99,
normalise_observations=False)
ppo_agent = create_ppo_agent(rl_env, gamma=0.90)
ppo_agent.initialize()
del rl_env
ppo_sim_agent = RLSimulationAgent(env, ppo_agent, normalise_obs=False)
assert ppo_sim_agent._normalise_obs is False
ppo_sim_agent = RLSimulationAgent(env, ppo_agent, normalise_obs=True)
assert ppo_sim_agent._normalise_obs is True
def test_rl_simulation_agent_discount_factor_reinforce():
"""
Tests that the discount factor is passed from a REINFORCE agent to an RLSimulationAgent
correctly.
"""
# Set up the agent as before.
seed = 72
env = load_scenario("single_server_queue", job_gen_seed=seed).env
rl_env, _ = rl_env_from_snc_env(env, discount_factor=0.99)
reinforce_agent = create_reinforce_agent(rl_env, gamma=0.97)
reinforce_agent.initialize()
del rl_env
reinforce_sim_agent = RLSimulationAgent(env,
reinforce_agent,
normalise_obs=True)
assert reinforce_sim_agent.discount_factor == 0.97
def test_rl_env_normalise_obs_action():
"""
Ensure that the normalise_obs property of RLControlledRandomWalk is used correctly.
"""
# Set the environment name for this case as the asserts are difficult to make as variables.
env_name = 'klimov_model'
# Set up the environment parameters.
# Environment parameters do not affect the test result here.
env = load_scenario(env_name,
job_gen_seed=10,
override_env_params={"initial_state": [100, 100, 100, 100]}).env
rl_env, _ = rl_env_from_snc_env(env, discount_factor=0.99, for_tf_agent=False)
assert rl_env.normalise_obs is True
s0_normalised = rl_env.reset()
assert s0_normalised.tolist() == [0.25, 0.25, 0.25, 0.25]
rl_env.normalise_obs = False
s0_unnormalised = rl_env.reset()
assert s0_unnormalised.tolist() == [100, 100, 100, 100]
def test_ppo_agent_init(env_name, expected_action_spec_shape):
"""
Tests agent set up and initialisation.
"""
# Set up environment using default parameters.
# Environment parameters do not affect the test result here.
tf_env, _ = rl_env_from_snc_env(load_scenario(env_name,
job_gen_seed=10)[1],
discount_factor=0.99,
normalise_observations=False)
# Instantiate and initialise a PPO agent for the environment.
ppo_agent = create_ppo_agent(tf_env, num_epochs=10)
ppo_agent.initialize()
# Validate initialisation by checking relevant properties of the initalised agent.
assert isinstance(ppo_agent.action_spec, BoundedTensorSpec)
assert ppo_agent.action_spec.shape == expected_action_spec_shape
assert ppo_agent.name == "PPO_Agent"
assert ppo_agent.time_step_spec == tf_env.time_step_spec()
def test_reinforce_agent_init(env_name, expected_action_spec_shape):
"""
Tests agent set up and initialisation.
"""
# Set up environment using default parameters.
# Environment parameters do not affect the test result here.
tf_env, _ = rl_env_from_snc_env(load_scenario(
env_name,
job_gen_seed=10,
override_env_params={'max_episode_length': 25})[1],
discount_factor=0.99)
# Instantiate and initialise a REINFORCE agent for the environment.
reinforce_agent = create_reinforce_agent(tf_env)
reinforce_agent.initialize()
# Validate initialisation by checking some properties of the initalised agent.
assert isinstance(reinforce_agent.action_spec, BoundedTensorSpec)
assert reinforce_agent.action_spec.shape == expected_action_spec_shape
assert reinforce_agent.name == "reinforce_agent"
assert reinforce_agent.time_step_spec == tf_env.time_step_spec()
def test_rl_simulation_agent_action_mapping():
"""
Tests that the RL Simulation Agent with the SNC interface is able to receive states and produce
actions both of the expected type and form.
"""
# Set up the agent as above
seed = 72
env = load_scenario("single_server_queue", job_gen_seed=seed).env
rl_env, _ = rl_env_from_snc_env(env, discount_factor=0.99)
rl_agent = create_reinforce_agent(rl_env)
rl_agent.initialize()
del rl_env
sim_agent = RLSimulationAgent(env, rl_agent, normalise_obs=True)
# Attain a state and form an action.
state = env.reset()
action = sim_agent.map_state_to_actions(state)
# Ensure that the action is as expected first with a formal assertion and then by passing it
# to the environment.
assert isinstance(action, snc_types.ActionProcess)
env.step(action)
def test_rl_simulation_agent_init():
"""
Test the intitalisation of an RL agent with an interface compatible with the SNC simulator.
"""
# To instantiate an agent from tf_agents we need an RL environment which itself requires a
# standard SNC environment. We therefore set up an SNC environment and then wrap it for the
# TensorFlow agent. This TF environment is later deleted since it is no longer required and to
# ensure that it is not used inadvertently.
seed = 72
env = load_scenario("single_server_queue", job_gen_seed=seed).env
rl_env, _ = rl_env_from_snc_env(env, discount_factor=0.99)
rl_agent = create_reinforce_agent(rl_env)
rl_agent.initialize()
del rl_env
# Wrapping the agent for the SNC simulator using information from the environment and the agent.
sim_agent = RLSimulationAgent(env, rl_agent, normalise_obs=True)
# Test that the agent has all of the attributed we want and that they are of the right type.
assert hasattr(sim_agent, "_rl_env") and isinstance(
sim_agent._rl_env, RLControlledRandomWalk)
assert hasattr(sim_agent, "_rl_agent") and isinstance(
sim_agent._rl_agent, TFAgent)
assert hasattr(sim_agent, "_policy") and isinstance(
sim_agent._policy, tf_policy.Base)
assert hasattr(sim_agent, "_is_eval_policy") and isinstance(
sim_agent._is_eval_policy, bool)
assert hasattr(sim_agent, "env") and isinstance(sim_agent.env,
ControlledRandomWalk)
assert hasattr(sim_agent, "buffer_processing_matrix") and isinstance(
sim_agent.buffer_processing_matrix, snc_types.BufferMatrix)
assert hasattr(sim_agent, "constituency_matrix") and isinstance(
sim_agent.constituency_matrix, snc_types.ConstituencyMatrix)
assert hasattr(sim_agent, "demand_rate") and isinstance(
sim_agent.demand_rate, np.ndarray)
assert hasattr(sim_agent,
"list_boundary_constraint_matrices") and isinstance(
sim_agent.list_boundary_constraint_matrices, list)
assert hasattr(sim_agent, "name") and isinstance(sim_agent.name, str)
def test_bellman_pets_agent_init(env_name, expected_action_spec_shape):
"""
Tests agent set up and initialisation.
"""
# Set up environment using default parameters.
# Environment parameters do not affect the test result here.
tf_env, _ = rl_env_from_snc_env(load_scenario(env_name,
job_gen_seed=10)[1],
discount_factor=0.99,
normalise_observations=False)
# Instantiate and initialise a PETS agent for the environment.
bellman_pets_agent = create_bellman_pets_agent(
env=tf_env,
reward_model_class=CRWRewardModel,
initial_state_distribution_model_class=CRWInitialStateModel,
)
# Validate initialisation by checking relevant properties of the initalised agent.
assert isinstance(bellman_pets_agent.action_spec, BoundedTensorSpec)
assert bellman_pets_agent.action_spec.shape == expected_action_spec_shape
assert bellman_pets_agent.name == "PETS_Agent"
assert bellman_pets_agent.time_step_spec == tf_env.time_step_spec()
def __init__(self,
env: ControlledRandomWalk,
agent: TFAgent,
normalise_obs: bool,
name: str = "RLSimulationAgent",
evaluation: bool = False):
"""
Sets up the simulation agent from an environment and a standard TensorFlow Agent.
Note: The environment is not used for simulation, simply for interpreting RL Agent actions.
:param env: The SNC environment for the simulation.
:param agent: The fully initialised (and trained) TensorFlow agent.
:param name: Agent identifier.
:param evaluation: Determines whether to use the greedy policy or not. Defaults to True i.e.
use greedy policy.
"""
# Attain an RLControlledRandomWalk instance from the ControlledRandomWalk provided.
# This is used to interpret actions from an RL Agent.
self.discount_factor = agent._discount_factor if isinstance(agent, PPOAgent) \
else agent._gamma
self._rl_env, _ = rl_env_from_snc_env(
env,
discount_factor=self.discount_factor,
for_tf_agent=False,
normalise_observations=normalise_obs)
# Set up private properties required for map_state_to_actions
self._rl_agent = agent
self._is_eval_policy = evaluation
self._normalise_obs = normalise_obs
self._policy = self._rl_agent.policy if self._is_eval_policy \
else self._rl_agent.collect_policy
# Call the standard initialiser.
super().__init__(env, name)
def load_rl_agent(
env: ControlledRandomWalk,
rl_algorithm: str,
load_path: str,
discount_factor: float = 0.99,
agent_params: Optional[Dict[str, Any]] = None) -> RLSimulationAgent:
"""
Instantiates an RL agent in the RLSimulationAgent interface for compatibility and loads the
weights from training into it.
:param env: The controlled random walk environment for which the agent is required.
:param rl_algorithm: The name of the RL algorithm used to train the agent.
:param load_path: Path to a directory where TensorFlow checkpoints have been saved (i.e. where
the model's weights are saved).
:param discount_factor: A scalar discount factor to pass to the agent.
:param agent_params: A dictionary of possible overrides for the default TF-Agents agent set up.
:return: An RL agent initialised with saved weights ready for evaluation.
"""
# Lazy import of TensorFlow as if no RL agent is run then it isn't needed.
import tensorflow as tf
# Attain a TensorFlow compatible version of the environment.
# We need a TensorFlow environment to initialise the agent correctly.
# First determine whether or not to normalise observations, PPO has its own normalisation so we
# only normalise for reinforce agents or PPO agents where normalisation is turned off.
normalise_obs = rl_algorithm == 'reinforce' or \
(rl_algorithm == 'ppo' and not agent_params.get('normalize_observations', True))
tf_env, _ = rl_env.rl_env_from_snc_env(
env, discount_factor, normalise_observations=normalise_obs)
# Set up an enumeration of functions which build agents to allow for extending to new agents.
# Pick out the correct RL agent from those we have implemented.
if rl_algorithm.lower() == 'reinforce':
agent = create_reinforce_agent(tf_env,
gamma=discount_factor,
agent_params=agent_params)
elif rl_algorithm.lower() == 'ppo':
agent = create_ppo_agent(tf_env,
gamma=discount_factor,
agent_params=agent_params)
else:
raise NotImplementedError(
"An agent using the RL algorithm requested is not yet implemented")
# Initialise the agent and load in parameters from the most recent save.
# Note that this can be adjusted to load in weights from any point in training (so long as they
# have been saved).
agent.initialize()
restorer = tf.train.Checkpoint(agent=agent)
restore_manager = tf.train.CheckpointManager(restorer,
directory=load_path,
max_to_keep=20)
restorer.listed = agent.trainable_variables
restoration = restorer.restore(restore_manager.latest_checkpoint)
restoration.run_restore_ops()
# Check that the weights have been loaded and that the model from which the weights were saved
# matches the model which they are being loaded into.
restoration.assert_nontrivial_match()
restoration.assert_existing_objects_matched()
# We name the agent in line with the checkpoint used to restore the weights. This aids in
# identifying which experiment run is being looked at from log files.
agent_name = f"RLSimulationAgent - {restore_manager.latest_checkpoint}"
# Finally wrap the agent for compatibility with the SNC simulator.
simulation_agent = RLSimulationAgent(env,
agent,
normalise_obs,
name=agent_name)
return simulation_agent
