# dqn.load_weights(checkpoint_weights_filename)
# elif os.path.isfile(weights_filename):
# print("Loading previous weights...")
# dqn.load_weights(weights_filename)
dqn.fit(env, callbacks=callbacks, nb_steps=20000000, log_interval=10000)
# After training is done, we save the final weights one more time.
dqn.save_weights(weights_filename, overwrite=True)
# Finally, evaluate our algorithm for 10 episodes.
dqn.test(env, nb_episodes=10, visualize=False)
elif args.mode == 'test':
weights_filename = 'wts/dqn_Breakout-v0_weights_12000000_phyran.h5f'.format(
args.env_name)
if args.weights:
weights_filename = args.weights
print(env.unwrapped.get_action_meanings())
np.random.seed(None)
env.seed(None)
dqn.load_weights(weights_filename)
dqn.training = False
dqn.test_policy = EpsilonPhysicsPolicy(
eps_phy=0.01, eps_ran=0.00
) # set a small epsilon for test policy to avoid getting stuck
env = gym.wrappers.Monitor(env,
"records/",
video_callable=lambda episode_id: True,
force=True)
dqn.test(env, nb_episodes=100, visualize=False)
env.close()
dqn.load_weights(weights_filename)
dqn.fit(env,
callbacks=callbacks,
nb_steps=20000000,
log_interval=10000,
nb_max_start_steps=20)
# After training is done, we save the final weights one more time.
dqn.save_weights(weights_filename, overwrite=True)
# Finally, evaluate our algorithm for 10 episodes.
dqn.test(env, nb_episodes=1, nb_max_start_steps=20, visualize=False)
elif args.mode == 'test':
weights_filename = 'wts/phy_dqn_BreakoutDeterministic-v4_weights.h5f'.format(
args.env_name)
if args.weights:
weights_filename = args.weights
np.random.seed(None)
env.seed(None)
print(env.unwrapped.get_action_meanings())
dqn.load_weights(weights_filename)
dqn.training = False
dqn.test_policy = EpsGreedyQPolicy(
0.01) # set a small epsilon for test policy to avoid getting stuck
env = gym.wrappers.Monitor(env,
"records/",
video_callable=lambda episode_id: True,
force=True)
dqn.test(env, nb_episodes=100, nb_max_start_steps=20, visualize=False)
env.close()