def test_agents(self):
params = ParameterServer()
params["ML"]["BaseAgent"]["NumSteps"] = 2
params["ML"]["BaseAgent"]["MaxEpisodeSteps"] = 2
bp = DiscreteHighwayBlueprint(params, num_scenarios=10, random_seed=0)
env = SingleAgentRuntime(blueprint=bp, render=False)
# IQN Agent
iqn_agent = IQNAgent(env=env, test_env=env, params=params)
env.ml_behavior = iqn_agent
self.assertEqual(env.ml_behavior.set_action_externally, False)
iqn_agent.run()
self.assertEqual(env.ml_behavior.set_action_externally, True)
# FQF Agent
fqf_agent = FQFAgent(env=env, test_env=env, params=params)
env.ml_behavior = fqf_agent
self.assertEqual(env.ml_behavior.set_action_externally, False)
fqf_agent.run()
self.assertEqual(env.ml_behavior.set_action_externally, True)
# QRDQN Agent
qrdqn_agent = QRDQNAgent(env=env, test_env=env, params=params)
env.ml_behavior = qrdqn_agent
self.assertEqual(env.ml_behavior.set_action_externally, False)
qrdqn_agent.run()
self.assertEqual(env.ml_behavior.set_action_externally, True)
def run_configuration(argv):
# params = ParameterServer(filename="examples/example_params/tfa_params.json")
params = ParameterServer()
# NOTE: Modify these paths in order to save the checkpoints and summaries
# params["ML"]["BehaviorTFAAgents"]["CheckpointPath"] = "YOUR_PATH"
# params["ML"]["TFARunner"]["SummaryPath"] = "YOUR_PATH"
params["World"]["remove_agents_out_of_map"] = True
# create environment
bp = ContinuousMergingBlueprint(params,
number_of_senarios=2500,
random_seed=0)
env = SingleAgentRuntime(blueprint=bp, render=False)
# PPO-agent
# ppo_agent = BehaviorPPOAgent(environment=env,
# params=params)
# env.ml_behavior = ppo_agent
# runner = PPORunner(params=params,
# environment=env,
# agent=ppo_agent)
# SAC-agent
sac_agent = BehaviorSACAgent(environment=env, params=params)
env.ml_behavior = sac_agent
runner = SACRunner(params=params, environment=env, agent=sac_agent)
if FLAGS.mode == "train":
runner.SetupSummaryWriter()
runner.Train()
elif FLAGS.mode == "visualize":
runner.Run(num_episodes=10, render=True)
elif FLAGS.mode == "evaluate":
runner.Run(num_episodes=100, render=False)
def test_behavior_wrapping(self):
# create scenario
params = ParameterServer()
bp = DiscreteHighwayBlueprint(params,
number_of_senarios=10,
random_seed=0)
env = SingleAgentRuntime(blueprint=bp, render=False)
#env = gym.make("highway-v1", params=params)
ml_behaviors = []
# ml_behaviors.append(IQNAgent(env=env, test_env=env, params=params))
ml_behaviors.append(FQFAgent(env=env, params=params))
# ml_behaviors.append(QRDQNAgent(env=env, test_env=env, params=params))
for ml_behavior in ml_behaviors:
# set agent
env.ml_behavior = ml_behavior
env.reset()
action = np.random.randint(low=0, high=env.action_space.n)
observed_next_state, reward, done, info = env.step(action)
print(
f"Observed state: {observed_next_state}, Reward: {reward}, Done: {done}"
)
# action is set externally
ml_behavior._set_action_externally = True
agent_id = list(env._world.agents.keys())[0]
observed_world = env._world.Observe([agent_id])[0]
# do a random action and plan trajectory
action = np.random.randint(low=1, high=env.action_space.n)
ml_behavior.ActionToBehavior(action)
a = ml_behavior.Plan(0.2, observed_world)
# sample another different random action
another_action = action
while another_action == action:
another_action = np.random.randint(low=1,
high=env.action_space.n)
# plan trajectory for the another action
ml_behavior.ActionToBehavior(another_action)
b = ml_behavior.Plan(0.2, observed_world)
# the trajectory generated by two different actions shoould be different
self.assertEqual(np.any(np.not_equal(a, b)), True)
# action will be calculated within the Plan(..) fct.
ml_behavior._set_action_externally = False
a = ml_behavior.Plan(0.2, observed_world)
b = ml_behavior.Plan(0.2, observed_world)
last_action = ml_behavior.GetLastAction()
self.assertTrue(isinstance(last_action, float))
# same trajectory for same state
np.testing.assert_array_equal(a, b)
def test_agent_and_runner(self):
params = ParameterServer()
bp = ContinuousHighwayBlueprint(params,
number_of_senarios=10,
random_seed=0)
env = SingleAgentRuntime(blueprint=bp, render=False)
agent = BehaviorPPOAgent(environment=env, params=params)
# set agent
env.ml_behavior = agent
runner = PPORunner(params=params, environment=env, agent=agent)
# runner.Train()
runner.Visualize()
def run_configuration(argv):
# Uncomment one of the following default parameter filename definitions,
# depending on which GNN library you'd like to use.
# File with standard parameters for tf2_gnn use:
# param_filename = "examples/example_params/tfa_sac_gnn_tf2_gnn_default.json"
# File with standard parameters for spektral use:
param_filename = "examples/example_params/tfa_sac_gnn_spektral_default.json"
params = ParameterServer(filename=param_filename)
# NOTE: Modify these paths to specify your preferred path for checkpoints and summaries
# params["ML"]["BehaviorTFAAgents"]["CheckpointPath"] = "YOUR_PATH"
# params["ML"]["TFARunner"]["SummaryPath"] = "YOUR_PATH"
#viewer = MPViewer(
# params=params,
# x_range=[-35, 35],
# y_range=[-35, 35],
# follow_agent_id=True)
#viewer = VideoRenderer(
# renderer=viewer,
# world_step_time=0.2,
# fig_path="/your_path_here/training/video/")
# create environment
bp = ContinuousHighwayBlueprint(params,
number_of_senarios=2500,
random_seed=0)
observer = GraphObserver(params=params)
env = SingleAgentRuntime(blueprint=bp, observer=observer, render=False)
sac_agent = BehaviorGraphSACAgent(environment=env,
observer=observer,
params=params)
env.ml_behavior = sac_agent
runner = SACRunner(params=params, environment=env, agent=sac_agent)
if FLAGS.mode == "train":
runner.SetupSummaryWriter()
runner.Train()
elif FLAGS.mode == "visualize":
runner.Visualize(5)
elif FLAGS.mode == "evaluate":
runner.Evaluate()
def test_sac_graph_agent(self):
params = ParameterServer()
bp = ContinuousMergingBlueprint(params,
number_of_senarios=2500,
random_seed=0)
observer = GraphObserver(params=params)
env = SingleAgentRuntime(blueprint=bp, observer=observer, render=False)
sac_agent = BehaviorGraphSACAgent(environment=env,
observer=observer,
params=params)
env.ml_behavior = sac_agent
env.reset()
eval_id = env._scenario._eval_agent_ids[0]
self.assertEqual(env._world.agents[eval_id].behavior_model, sac_agent)
for _ in range(0, 5):
env._world.Step(0.2)
def test_configurable_blueprint(self):
params = ParameterServer(
filename="bark_ml/tests/data/highway_merge_configurable.json")
# continuous model
ml_behavior = BehaviorContinuousML(params=params)
bp = ConfigurableScenarioBlueprint(params=params,
ml_behavior=ml_behavior)
env = SingleAgentRuntime(blueprint=bp, render=False)
# agent
sac_agent = BehaviorSACAgent(environment=env, params=params)
env.ml_behavior = sac_agent
# test run
env.reset()
for _ in range(0, 5):
action = np.random.randint(low=0, high=3)
observed_next_state, reward, done, info = env.step(action)
def test_tracing_bark_world(self):
params = ParameterServer()
bp = ContinuousHighwayBlueprint(params)
tracer = Tracer()
env = SingleAgentRuntime(blueprint=bp, render=False)
sac_agent = BehaviorSACAgent(environment=env, params=params)
env.ml_behavior = sac_agent
# NOTE: this also tests if a BARK agent is self-contained
env.ml_behavior.set_actions_externally = False
env.reset()
bark_world = env._world
for j in range(0, 2):
for i in range(0, 5):
bark_world.Step(0.2)
eval_dict = bark_world.Evaluate()
tracer.Trace(eval_dict, num_episode=j)
self.assertEqual(len(tracer._states), 10)
def run_configuration(argv):
""" Main """
params = ParameterServer(
filename="examples/example_params/tfa_generate_params.json")
# params = ParameterServer()
output_dir = params["GenerateExpertTrajectories"]["OutputDirectory"]
# create environment
blueprint = params["World"]["Blueprint"]
if blueprint == 'merging':
bp = ContinuousMergingBlueprint(params,
number_of_senarios=2500,
random_seed=0)
elif blueprint == 'highway':
bp = ContinuousHighwayBlueprint(params,
number_of_senarios=2500,
random_seed=0)
else:
raise ValueError(f'{blueprint} is no valid blueprint.')
env = SingleAgentRuntime(blueprint=bp, render=False)
sac_agent = BehaviorSACAgent(environment=env, params=params)
env.ml_behavior = sac_agent
runner = SACRunnerGenerator(params=params,
environment=env,
agent=sac_agent)
if FLAGS.mode == "train":
runner.SetupSummaryWriter()
runner.Train()
elif FLAGS.mode == "visualize":
runner.Visualize(params["Visualization"]["NumberOfEpisodes"])
elif FLAGS.mode == "generate":
expert_trajectories = runner.GenerateExpertTrajectories(
num_trajectories=params["GenerateExpertTrajectories"]
["NumberOfTrajectories"],
render=params["World"]["render"])
save_expert_trajectories(output_dir=output_dir,
expert_trajectories=expert_trajectories)
# store all used params of the training
# params.Save(os.path.join(Path.home(), "examples/example_params/tfa_params.json"))
sys.exit(0)
def test_agent_and_runner(self):
params = ParameterServer()
bp = ContinuousHighwayBlueprint(params,
num_scenarios=10,
random_seed=0)
env = SingleAgentRuntime(blueprint=bp, render=False)
agent = BehaviorPPOAgent(environment=env, params=params)
# set agent
params["ML"]["PPORunner"]["NumberOfCollections"] = 2
params["ML"]["SACRunner"]["NumberOfCollections"] = 2
params["ML"]["TFARunner"]["EvaluationSteps"] = 2
env.ml_behavior = agent
self.assertEqual(env.ml_behavior.set_action_externally, False)
runner = PPORunner(params=params, environment=env, agent=agent)
runner.Train()
self.assertEqual(env.ml_behavior.set_action_externally, True)
runner.Run()
self.assertEqual(env.ml_behavior.set_action_externally, True)
def run_configuration(argv):
params = ParameterServer(
filename="examples/example_params/tfa_params.json")
# params = ParameterServer()
# NOTE: Modify these paths in order to save the checkpoints and summaries
# params["ML"]["BehaviorTFAAgents"]["CheckpointPath"] = "/Users/hart/Development/bark-ml/checkpoints_merging_nn/"
# params["ML"]["TFARunner"]["SummaryPath"] = "/Users/hart/Development/bark-ml/checkpoints_merging_nn/"
params["Visualization"]["Agents"]["Alpha"]["Other"] = 0.2
params["Visualization"]["Agents"]["Alpha"]["Controlled"] = 0.2
params["Visualization"]["Agents"]["Alpha"]["Controlled"] = 0.2
params["ML"]["VisualizeCfWorlds"] = False
params["ML"]["VisualizeCfHeatmap"] = True
params["World"]["remove_agents_out_of_map"] = False
viewer = MPViewer(params=params,
x_range=[-35, 35],
y_range=[-35, 35],
follow_agent_id=True)
# create environment
bp = ContinuousMergingBlueprint(params, num_scenarios=10000, random_seed=0)
env = SingleAgentRuntime(blueprint=bp, render=False, viewer=viewer)
# PPO-agent
# ppo_agent = BehaviorPPOAgent(environment=env,
# params=params)
# env.ml_behavior = ppo_agent
# runner = PPORunner(params=params,
# environment=env,
# agent=ppo_agent)
# SAC-agent
sac_agent = BehaviorSACAgent(environment=env, params=params)
env.ml_behavior = sac_agent
runner = SACRunner(params=params, environment=env, agent=sac_agent)
if FLAGS.mode == "train":
runner.SetupSummaryWriter()
runner.Train()
elif FLAGS.mode == "visualize":
runner.Run(num_episodes=50, render=True)
elif FLAGS.mode == "evaluate":
runner.Run(num_episodes=100, render=False)
def test_behavior_wrapping(self):
# create scenario
params = ParameterServer()
bp = ContinuousHighwayBlueprint(params,
num_scenarios=10,
random_seed=0)
env = SingleAgentRuntime(blueprint=bp, render=False)
ml_behaviors = []
ml_behaviors.append(BehaviorPPOAgent(environment=env, params=params))
ml_behaviors.append(BehaviorSACAgent(environment=env, params=params))
for ml_behavior in ml_behaviors:
# set agent
env.ml_behavior = ml_behavior
env.reset()
done = False
while done is False:
action = np.random.uniform(low=-0.1, high=0.1, size=(2, ))
observed_next_state, reward, done, info = env.step(action)
print(
f"Observed state: {observed_next_state}, Reward: {reward}, Done: {done}"
)
# action is set externally
ml_behavior._set_action_externally = True
agent_id = list(env._world.agents.keys())[0]
observed_world = env._world.Observe([agent_id])[0]
action = np.random.uniform(low=-0.1, high=0.1, size=(2, ))
ml_behavior.ActionToBehavior(action)
a = ml_behavior.Plan(0.2, observed_world)
action = np.random.uniform(low=-0.1, high=0.1, size=(2, ))
ml_behavior.ActionToBehavior(action)
b = ml_behavior.Plan(0.2, observed_world)
self.assertEqual(np.any(np.not_equal(a, b)), True)
# action will be calculated within the Plan(..) fct.
a = ml_behavior.Plan(0.2, observed_world)
b = ml_behavior.Plan(0.2, observed_world)
np.testing.assert_array_equal(a, b)
def test_behavior_wrapping(self):
# create scenario
params = ParameterServer()
bp = ContinuousHighwayBlueprint(params,
number_of_senarios=10,
random_seed=0)
env = SingleAgentRuntime(blueprint=bp, render=True)
ml_behaviors = []
ml_behaviors.append(BehaviorPPOAgent(environment=env, params=params))
ml_behaviors.append(BehaviorSACAgent(environment=env, params=params))
for ml_behavior in ml_behaviors:
# set agent
env.ml_behavior = ml_behavior
env.reset()
done = False
while done is False:
action = np.random.uniform(low=-0.1, high=0.1, size=(2, ))
observed_next_state, reward, done, info = env.step(action)
print(
f"Observed state: {observed_next_state}, Reward: {reward}, Done: {done}"
)
def run_configuration(argv):
params = ParameterServer()
# NOTE: Modify these paths to specify your preferred path for checkpoints and summaries
# params["ML"]["BehaviorTFAAgents"]["CheckpointPath"] = "/Users/hart/Development/bark-ml/checkpoints_merge_spektral_att2/"
# params["ML"]["TFARunner"]["SummaryPath"] = "/Users/hart/Development/bark-ml/checkpoints_merge_spektral_att2/"
#viewer = MPViewer(
# params=params,
# x_range=[-35, 35],
# y_range=[-35, 35],
# follow_agent_id=True)
#viewer = VideoRenderer(
# renderer=viewer,
# world_step_time=0.2,
# fig_path="/your_path_here/training/video/")
# create environment
bp = ContinuousMergingBlueprint(params, num_scenarios=2500, random_seed=0)
observer = GraphObserver(params=params)
env = SingleAgentRuntime(blueprint=bp, observer=observer, render=False)
sac_agent = BehaviorGraphSACAgent(environment=env,
observer=observer,
params=params,
init_gnn='init_interaction_network')
env.ml_behavior = sac_agent
runner = SACRunner(params=params, environment=env, agent=sac_agent)
if FLAGS.mode == "train":
runner.SetupSummaryWriter()
runner.Train()
elif FLAGS.mode == "visualize":
runner.Run(num_episodes=10, render=True)
elif FLAGS.mode == "evaluate":
runner.Run(num_episodes=250, render=False)
