config["target_network_update_freq"] = 500000
config["exploration_config"] = {"type": "PerWorkerEpsilonGreedy"}
config["worker_side_prioritization"] = True
# config["min_iter_time_s"] = 30
# config["training_intensity"] = None
# config["log_level"] = 'DEBUG'
config["env_config"] = env_config
trainer = DQNTrainer(config=config, env=SSA_Tasker_Env)
# Can optionally call trainer.restore(path) to load a checkpoint.
checkpoints = []
result = {'timesteps_total': 0}
i = 0
while result['timesteps_total'] < 1e7:
# Perform one iteration of training the policy with PPO
result = trainer.train()
print(pretty_print(result))
if result['training_iteration'] % 4 == 0:
checkpoint = trainer.save()
print("checkpoint saved at", checkpoint)
checkpoints.append(copy(checkpoint))
# path = '/home/ash/ray_results/DQN_SSA_Tasker_Env_2020-08-13_13-26-22s_wgpxq5/checkpoint_1/'
# trainer.restore(path)
# trainer.import_model("my_weights.h5")
'''
ray.shutdown()
'''
elif args.run == "PG":
agent = PGTrainer(
env="srv",
config={
"num_workers": 0,
"env_config": {
# Use the connector server to generate experiences.
"input": (
lambda ioctx: PolicyServerInput(ioctx, SERVER_ADDRESS, SERVER_PORT)
),
"observation_size": args.observation_size,
"action_size": args.action_size,
},
})
else:
raise ValueError("--run must be DQN or PG")
# Attempt to restore from checkpoint if possible.
if os.path.exists(args.checkpoint_file):
checkpoint_file = open(args.checkpoint_file).read()
print("Restoring from checkpoint path", checkpoint_file)
agent.restore(checkpoint_file)
# Serving and training loop
while True:
print(pretty_print(agent.train()))
checkpoint_file = agent.save()
print("Last checkpoint", checkpoint_file)
with open(args.checkpoint_file, "w") as f:
f.write(checkpoint_file)
# else:
# return "dqn_policy"
return agent_id
dqn_trainer = DQNTrainer(env="cityflow_multi",
config={
"multiagent": {
"policies":
policies,
"policy_mapping_fn":
policy_mapping_fn,
"policies_to_train":
[id_ for id_ in intersection_id]
},
"gamma": 0.95,
"n_step": 3,
"num_workers": 1,
"num_cpus_per_worker": 20,
"env_config": config
})
for i in range(args.epoch):
print("== Iteration", i, "==")
# improve the DQN policy
print("-- DQN --")
print(pretty_print(dqn_trainer.train()))
if (i + 1) % 100 == 0:
checkpoint = dqn_trainer.save()
print("checkpoint saved at", checkpoint)