network='auto',
memory=10000,
)
else:
print("Available agents: vpg, ppo, dqn")
exit()
print("agent ready", agent)
agent.initialize() # Set up base of agent
try: # Looks to see if a saved model is available and loads it
lastEpoch = int(
os.listdir(tmp + "/saved/player_pun/" + args.agent)[2].split("-")[0])
agent.restore(directory=tmp + "/saved/player_pun/" + args.agent)
print("restored")
except Exception as e: # starts fresh if no saved model is available
print("DID NOT RESTORE")
lastEpoch = 0
epochs = 2000000
for epoch in tqdm(range(lastEpoch, epochs + 1)):
#print(epoch)
#pdb.set_trace()
map_num = random.randint(1, 20)
simulation = GameSim(map_num)
# penalize over-estimation
#print('THIS IS A RESET OF THE SHIT')
saved_pos = None
memory=10000,
)
else:
print("Available agents: vpg, ppo, dqn")
exit()
print("agent ready", agent)
agent.initialize() # Set up base of agent
try: # Looks to see if a saved model is available and loads it
lastEpoch = int(os.listdir(tmp + "/saved/sim_rew_mult/" +
args.agent)[2].split("-")[0])
agent.restore(directory=tmp + "/saved/sim_rew_mult/" + args.agent)
print("restored")
except Exception as e: # starts fresh if no saved model is available
print(e)
lastEpoch = 0
epochs = 100000
level_index = 1
for epoch in tqdm(range(lastEpoch, epochs)):
# print(epoch)
simulation = GameSim(level_index)
# penalize over-estimation
saved_pos = [None]*NUMBER_OF_PLAYERS
dict(type="dense", size=32),
],
memory=10000,
)
print("agent ready", agent)
if args.process == "train":
new_agent = copy.deepcopy(agent)
agent.initialize()
try:
lastEpoch = int(os.listdir("saved/" + args.agent)[2].split("-")[0])
agent.restore(directory="saved/" + args.agent + "/" + args.contrarian)
print("restored")
except:
lastEpoch = 0
epochs = 100000
cluster_vals = []
for epoch in tqdm(range(lastEpoch, epochs)):
G = Audience(20, 15)
#20 reccomendations for every user
training_size = G.graph.shape[0] * 20
changes = []
for step in range(training_size):
action = agent.act(G.graph)
class TensorForceAgent(BaseAgent):
"""The TensorForceAgent. Acts through the algorith, not here."""
def __init__(self, character=characters.Bomber, algorithm='ppo'):
super(TensorForceAgent, self).__init__(character)
self.algorithm = algorithm
self.tf_agent = None
self.agent_id = 0
self.env = None
def act(self, obs, action_space):
ppo_state = self.envWrapper(obs)
return self.tf_agent.act(ppo_state)
def initialize(self, env, parallel_interactions=1, summarizer=None, saver=None):
from gym import spaces
from tensorforce.agents import PPOAgent
self.env = env
if self.algorithm == "ppo":
if type(env.action_space) == spaces.Tuple:
actions = {
str(num): {
'type': int,
'num_values': space.n
}
for num, space in enumerate(env.action_space.spaces)
}
else:
actions = dict(type='int', num_values=env.action_space.n)
self.tf_agent = PPOAgent(
states=dict(type='float', shape=env.observation_space.shape),
actions=actions,
max_episode_timesteps=2000,
network=[
dict(type='dense', size=64),
dict(type='dense', size=64)
],
# critic_network=[
# dict(type='dense', size=64),
# dict(type='dense', size=64)
# ],
parallel_interactions=parallel_interactions,
summarizer=summarizer,
saver=saver,
#execution={'num_parallel':64, 'type': 'single', 'session_config':None, 'distributed_spec':None},
batch_size=10)
# batching_capacity=1000,
# step_optimizer=dict(type='adam', learning_rate=1e-4))
return self.tf_agent
return None
def set_agent_id(self, agent_id):
self.agent_id = agent_id
def restore_model(self, directory=None,filename=None):
self.tf_agent.restore(directory=directory,filename=filename)
def envWrapper(self, state):
return self.env.featurize(state)
