def test_gnn_parameters(self):
params = ParameterServer()
params["ML"]["BehaviorGraphSACAgent"]["GNN"]["NumMpLayers"] = 4
params["ML"]["BehaviorGraphSACAgent"]["GNN"]["MpLayerNumUnits"] = 64
params["ML"]["BehaviorGraphSACAgent"]["GNN"]["message_calculation_class"] = "gnn_edge_mlp"
params["ML"]["BehaviorGraphSACAgent"]["GNN"]["global_exchange_mode"] = "mean"
gnn_library = GNNWrapper.SupportedLibrary.spektral
params["ML"]["BehaviorGraphSACAgent"]["GNN"]["Library"] = gnn_library
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)
actor_gnn = sac_agent._agent._actor_network._gnn
critic_gnn = sac_agent._agent._critic_network_1._gnn
for gnn in [actor_gnn, critic_gnn]:
self.assertEqual(gnn._params["NumMpLayers"], 4)
self.assertEqual(gnn._params["MpLayerNumUnits"], 64)
self.assertEqual(gnn._params["message_calculation_class"], "gnn_edge_mlp")
self.assertEqual(gnn._params["global_exchange_mode"], "mean")
self.assertEqual(gnn._params["Library"], gnn_library)
def configurable_setup(params, num_scenarios, graph_sac=True):
"""Configurable GNN setup depending on a given filename
Args:
params: ParameterServer instance
Returns:
observer: GraphObserver instance
actor: ActorNetwork of BehaviorGraphSACAgent
"""
observer = GraphObserver(params=params)
bp = ContinuousHighwayBlueprint(params,
number_of_senarios=num_scenarios,
random_seed=0)
env = SingleAgentRuntime(blueprint=bp, observer=observer,
render=False)
if graph_sac:
# Get GNN SAC actor net
sac_agent = BehaviorGraphSACAgent(environment=env, observer=observer,
params=params)
else:
sac_agent = BehaviorSACAgent(environment=env, params=params)
actor = sac_agent._agent._actor_network
return observer, actor
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 _configurable_setup(self, params_filename):
"""Configurable GNN setup depending on a given filename
Args:
params_filename: str, corresponds to path of params file
Returns:
params: ParameterServer instance
observer: GraphObserver instance
actor: ActorNetwork of BehaviorGraphSACAgent
"""
params = ParameterServer(filename=params_filename)
observer = GraphObserver(params=params)
bp = ContinuousHighwayBlueprint(params,
number_of_senarios=2,
random_seed=0)
env = SingleAgentRuntime(blueprint=bp, observer=observer, render=False)
# Get GNN SAC actor net
sac_agent = BehaviorGraphSACAgent(environment=env,
observer=observer,
params=params)
actor = sac_agent._agent._actor_network
return params, observer, actor
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)
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/"
# params["ML"]["TFARunner"]["SummaryPath"] = "/Users/hart/Development/bark-ml/checkpoints/"
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"] = False
# params["ML"]["ResultsFolder"] = "/Users/hart/Development/bark-ml/results/data/"
# viewer = MPViewer(
# params=params,
# x_range=[-35, 35],
# y_range=[-35, 35],
# follow_agent_id=True)
# create environment
bp = ContinuousMergingBlueprint(params,
num_scenarios=2500,
random_seed=0)
observer = GraphObserver(params=params)
behavior_model_pool = []
for count, a in enumerate([-5., 0., 5.]):
local_params = params.AddChild("local_"+str(count))
local_params["BehaviorConstantAcceleration"]["ConstAcceleration"] = a
behavior = BehaviorConstantAcceleration(local_params)
behavior_model_pool.append(behavior)
env = CounterfactualRuntime(
blueprint=bp,
observer=observer,
render=False,
params=params,
behavior_model_pool=behavior_model_pool)
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._environment._max_col_rate = 0.
runner.Run(num_episodes=1, render=True)
elif FLAGS.mode == "evaluate":
for cr in np.arange(0, 1, 0.1):
runner._environment._max_col_rate = cr
runner.Run(num_episodes=250, render=False, max_col_rate=cr)
runner._environment._tracer.Save(
params["ML"]["ResultsFolder"] + "evaluation_results_runtime.pckl")
goal_reached = runner._tracer.success_rate
runner._tracer.Save(
params["ML"]["ResultsFolder"] + "evaluation_results_runner.pckl")