from agents.common.networks.cnn_deepmind import CNNDeepmind
from agents.qrdqn.qrdqn import QRDQN
from agents.common.atari_wrappers import make_atari, wrap_deepmind
import pickle
import numpy as np
import matplotlib.pyplot as plt
notes = "This is a test run."
env_name = "BreakoutNoFrameskip-v4"
env = make_atari(env_name,noop=True)
env = wrap_deepmind(env, episode_life=True)
nb_steps = 12500000
agent = QRDQN( env,
CNNDeepmind,
n_quantiles=200,
kappa=0,
replay_start_size=50000,
replay_buffer_size=1000000,
gamma=0.99,
update_target_frequency=10000,
minibatch_size=32,
learning_rate=5e-5,
initial_exploration_rate=1,
final_exploration_rate=0.01,
final_exploration_step=1000000,
adam_epsilon=0.01/32,
from agents.common.networks import CNNDeepmind_Multihead
from agents.eqrdqn.eqrdqn import EQRDQN
from agents.common.atari_wrappers import make_atari, wrap_deepmind
import pickle
import numpy as np
import matplotlib.pyplot as plt
notes = "This is a test run."
env = make_atari("BreakoutNoFrameskip-v0", noop=False)
env = wrap_deepmind(env, episode_life=False)
nb_steps = 12500000
agent = EQRDQN(env,
CNNDeepmind_Multihead,
n_quantiles=200,
kappa=0,
prior=0.0001,
replay_start_size=50000,
replay_buffer_size=1000000,
gamma=0.99,
update_target_frequency=10000,
minibatch_size=32,
learning_rate=5e-5,
adam_epsilon=0.01 / 32,
update_frequency=4,
log_folder_details="Breakout-EQRDQN",
logging=True,
notes=notes)
import gym
import matplotlib.pyplot as plt
import numpy as np
from agents.eqrdqn.eqrdqn import EQRDQN
from agents.common.networks.cnn_deepmind import CNNDeepmind_Multihead
from agents.common.atari_wrappers import make_atari, wrap_deepmind
FOLDER = "???"
game_scores = []
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
env = make_atari("BreakoutNoFrameskip", noop=True)
env = wrap_deepmind(env, clip_rewards=False, episode_life=False)
agent = EQRDQN(env, CNNDeepmind_Multihead, n_quantiles=200)
for i in range(50):
filename = "network_" + str((i + 1) * 250000) + ".pth"
agent.load(FOLDER + filename)
score = 0
scores = []
total_timesteps = 0
while total_timesteps < 125000:
done = False
import gym
import matplotlib.pyplot as plt
import numpy as np
from agents.ide.ide import IDE
from agents.common.networks.cnn_deepmind import CNNDeepmind_Multihead
from agents.common.atari_wrappers import make_atari, wrap_deepmind
FOLDER = "???"
game_scores = []
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
env = make_atari("AsterixNoFrameskip", noop=True)
env = wrap_deepmind(env, clip_rewards=False, episode_life=False)
agent = IDE(env, CNNDeepmind_Multihead, n_quantiles=200)
for i in range(50):
filename = "network_" + str((i + 1) * 250000) + ".pth"
agent.load(FOLDER + filename)
score = 0
scores = []
total_timesteps = 0
while total_timesteps < 125000:
done = False
import gym
import matplotlib.pyplot as plt
import numpy as np
from agents.bootstrapped.bootstrapped import BOOTSTRAPPED
from agents.common.networks.cnn_deepmind import CNNDeepmind_k_head
from agents.common.atari_wrappers import make_atari, wrap_deepmind
folder = "results/2020-01-10-105609-AlienNoFrameskip-v4-bootstrapped/"
game_scores = []
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
env = make_atari("AlienNoFrameskip-v4", noop=True)
env = wrap_deepmind(env, clip_rewards=False, episode_life=False)
agent = BOOTSTRAPPED(env, CNNDeepmind_k_head, n_heads=10)
for i in range(50):
# filename = "network_" + str((i+1)*250000) + ".pth"
filename = "network_" + str(25) + '.pth'
agent.load(folder + filename)
score = 0
scores = []
total_timesteps = 0
while total_timesteps < 125000:
done = False