def main(args=None):
# Parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# Check if a GPU ID was set
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
set_session(get_session())
# Environment Initialization
if args.is_ai2thor:
config_dict = {'max_episode_length': 2000}
env = AI2ThorEnv(config_dict=config_dict)
env.reset()
state = env.reset()
state_dim = state.shape
action_dim = env.action_space.n
elif (args.is_atari):
# Atari Environment Wrapper
env = AtariEnvironment(args)
state_dim = env.get_state_size()
action_dim = env.get_action_size()
else:
# Standard Environments
env = Environment(gym.make(args.env), args.consecutive_frames)
env.reset()
state_dim = env.get_state_size()
action_dim = gym.make(args.env).action_space.n
algo = A3C(action_dim, state_dim, args.consecutive_frames, is_atari=args.is_atari, is_ai2thor=args.is_ai2thor)
algo.load_weights(args.actor_path, args.critic_path)
# Display agent
old_state, time = env.reset(), 0
while True:
a = algo.policy_action(old_state)
old_state, r, done, _ = env.step(a)
time += 1
if done:
print('----- done, resetting env ----')
env.reset()
def main(args=None):
# Parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# Check if a GPU ID was set
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
set_session(get_session())
# Environment Initialization
if(args.is_atari):
# Atari Environment Wrapper
env = AtariEnvironment(args)
state_dim = env.get_state_size()
action_dim = env.get_action_size()
elif(args.type=="DDPG"):
# Continuous Environments Wrapper
env = Environment(gym.make(args.env), args.consecutive_frames)
env.reset()
state_dim = env.get_state_size()
action_space = gym.make(args.env).action_space
action_dim = action_space.high.shape[0]
act_range = action_space.high
else:
# Standard Environments
env = Environment(gym.make(args.env), args.consecutive_frames)
env.reset()
state_dim = env.get_state_size()
action_dim = gym.make(args.env).action_space.n
# Pick algorithm to train
if(args.type=="DDQN"):
algo = DDQN(action_dim, state_dim, args)
algo.load_weights(args.model_path)
elif(args.type=="A2C"):
algo = A2C(action_dim, state_dim, args.consecutive_frames)
algo.load_weights(args.actor_path, args.critic_path)
elif(args.type=="A3C"):
algo = A3C(action_dim, state_dim, args.consecutive_frames, is_atari=args.is_atari)
algo.load_weights(args.actor_path, args.critic_path)
elif(args.type=="DDPG"):
algo = DDPG(action_dim, state_dim, act_range, args.consecutive_frames)
algo.load_weights(args.actor_path, args.critic_path)
# Display agent
old_state, time = env.reset(), 0
while True:
env.render()
a = algo.policy_action(old_state)
old_state, r, done, _ = env.step(a)
time += 1
if done: env.reset()
env.env.close()
def main(args=None):
# Parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
if args.wandb:
wandb.init(entity=args.wandb_id, project=args.wandb_project)
# Environment Initialization
if (args.is_atari):
# Atari Environment Wrapper
env = AtariEnvironment(args)
state_dim = env.get_state_size()
action_dim = env.get_action_size()
elif (args.type == "DDPG"):
# Continuous Environments Wrapper
env = Environment(gym.make(args.env), args.consecutive_frames)
env.reset()
state_dim = env.get_state_size()
action_space = gym.make(args.env).action_space
action_dim = action_space.high.shape[0]
act_range = action_space.high
else:
# Standard Environments
env = Environment(gym.make(args.env), args.consecutive_frames)
env.reset()
state_dim = env.get_state_size()
action_dim = gym.make(args.env).action_space.n
# Pick algorithm to train
if (args.type == "DDQN"):
algo = DDQN(action_dim, state_dim, args)
elif (args.type == "A2C"):
algo = A2C(action_dim, state_dim, args.consecutive_frames)
elif (args.type == "A3C"):
algo = A3C(action_dim,
state_dim,
args.consecutive_frames,
is_atari=args.is_atari)
elif (args.type == "DDPG"):
algo = DDPG(action_dim, state_dim, act_range, args.consecutive_frames)
# Train
stats = algo.train(env, args) # e, mean, stdev: e is episode
# Export results to CSV
if (args.gather_stats):
df = pd.DataFrame(np.array(stats))
df.to_csv(args.type + "/logs.csv",
header=['Episode', 'Mean', 'Stddev'],
float_format='%10.5f')
# Save weights and close environments
exp_dir = '{}/models/'.format(args.type)
if not os.path.exists(exp_dir):
os.makedirs(exp_dir)
export_path = '{}{}_ENV_{}_NB_EP_{}_BS_{}'.format(exp_dir, args.type,
args.env,
args.nb_episodes,
args.batch_size)
algo.save_weights(export_path)
env.env.close()
def main(args=None):
# Parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# Check if a GPU ID was set
# if args.gpu:
# os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
# sess = get_session()
# set_session(sess)
# K.set_session(sess)
# with tf.device('/gpu:0'):
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
# sess = tf.Session(config=config)
# K.set_session(sess)
# set_session(sess)
summary_writer = tf.summary.FileWriter(args.type + "/tensorboard_" +
args.env)
# Environment Initialization
if (args.is_atari):
# Atari Environment Wrapper
env = AtariEnvironment(args)
state_dim = env.get_state_size()
action_dim = env.get_action_size()
elif (args.type == "DDPG"):
# Continuous Environments Wrapper
# env = Environment(gym.make(args.env), args.consecutive_frames)
# env.reset()
# state_dim = env.get_state_size()
# action_space = gym.make(args.env).action_space
# action_dim = action_space.high.shape[0]
# act_range = action_space.high
env_before = gym.make(args.env)
env_unwrapped = env_before.unwrapped
# env_unwrapped.observation_space = env_unwrapped.observation_shape
# state_dim = env_unwrapped.observation_space.shape
# action_dim = env_unwrapped.action_space.n
env = Environment(env_before, args.consecutive_frames)
env.reset()
state_dim = env.get_state_size()
# action_space = env.action_space
# action_dim = env.get_action_size()
action_dim = env_unwrapped.action_space.shape[0]
act_range = env_unwrapped.action_space.high
print('state: ', state_dim)
print('action: ', action_dim)
print('act range', act_range)
else:
# Standard Environments
# env = Environment(gym.make(args.env), args.consecutive_frames)
# env.reset()
# state_dim = env.get_state_size()
# action_dim = gym.make(args.env).action_space.n
#unreal
env_before = gym.make(args.env)
# env_unwrapped = env_before.unwrapped
# env_unwrapped.observation_space = env_unwrapped.observation_shape
# state_dim = env_unwrapped.observation_space.shape
# action_dim = env_unwrapped.action_space.n
env = Environment(env_before, args.consecutive_frames)
env.reset()
state_dim = env.get_state_size()
action_dim = env.get_action_size()
print('state: ', state_dim)
print('action: ', action_dim)
# state_dim= (640,380)
# action_dim =3
# Pick algorithm to train
print('args type: ', args.type)
if (args.type == "DDQN"):
algo = DDQN(action_dim, state_dim, args)
elif (args.type == "A2C"):
algo = A2C(action_dim, state_dim, args.consecutive_frames)
elif (args.type == "A3C"):
algo = A3C(action_dim,
state_dim,
args.consecutive_frames,
is_atari=args.is_atari)
elif (args.type == "DDPG"):
algo = DDPG(args, action_dim, state_dim, act_range,
args.consecutive_frames)
if args.pretrain:
print('pretrain')
algo.load_weights(args.weights_path)
# Train
stats = algo.train(env, args, summary_writer)
# Export results to CSV
if (args.gather_stats):
df = pd.DataFrame(np.array(stats))
df.to_csv(args.type + "/logs.csv",
header=['Episode', 'Mean', 'Stddev'],
float_format='%10.5f')
# Save weights and close environments
exp_dir = '{}/models/'.format(args.type)
if not os.path.exists(exp_dir):
os.makedirs(exp_dir)
export_path = '{}{}_ENV_{}_NB_EP_{}_BS_{}'.format(exp_dir, args.type,
args.env,
args.nb_episodes,
args.batch_size)
algo.save_weights(export_path)
env.env.close()
for seed_ in [10]: #, 50, 100, 200, 500]:
seed(seed_)
set_random_seed(seed_)
print("Seed: ", seed_)
episode = 0
# run for 100 episodes
# Note: Please adjust this as needed to work with your model architecture.
# Make sure you still call evaluate() with the reward received in each episode
for i in range(wandb.config.episodes):
# Set reward received in this episode = 0 at the start of the episode
episodic_reward = 0
reset = False
# play a random game
state = env.reset()
done = False
while not done:
env.render()
sreward = 0
reward = 0
action = agent.predict(np.expand_dims(state, axis=0))
action = np.argmax(action)
#action = np.random.choice(np.arange(action_dim), p=action[0])
# perform the action and fetch next state, reward
state, reward, done, _ = env.step(action)
def main(args=None):
# Parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# Check if a GPU ID was set
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
set_session(get_session())
summary_writer = tf.summary.FileWriter(args.type + "/tensorboard_" + args.env)
# Environment Initialization
if(args.is_atari):
# Atari Environment Wrapper
env = AtariEnvironment(args)
state_dim = env.get_state_size()
action_dim = env.get_action_size()
elif(args.type=="DDPG"):
# Continuous Environments Wrapper
env = Environment(gym.make(args.env), args.consecutive_frames)
env.reset()
state_dim = env.get_state_size()
action_space = gym.make(args.env).action_space
action_dim = action_space.high.shape[0]
act_range = action_space.high
else:
# Standard Environments
env = Environment(gym.make(args.env), args.consecutive_frames)
env.reset()
state_dim = env.get_state_size()
action_dim = gym.make(args.env).action_space.n
# Pick algorithm to train
if(args.type=="DDQN"):
algo = DDQN(action_dim, state_dim, args)
elif(args.type=="A2C"):
algo = A2C(action_dim, state_dim, args.consecutive_frames)
elif(args.type=="A3C"):
algo = A3C(action_dim, state_dim, args.consecutive_frames, is_atari=args.is_atari)
elif(args.type=="DDPG"):
algo = DDPG(action_dim, state_dim, act_range, args.consecutive_frames)
# Train
stats = algo.train(env, args, summary_writer)
# Export results to CSV
if(args.gather_stats):
df = pd.DataFrame(np.array(stats))
df.to_csv(args.type + "/logs.csv", header=['Episode', 'Mean', 'Stddev'], float_format='%10.5f')
# Save weights and close environments
exp_dir = '{}/models/'.format(args.type)
if not os.path.exists(exp_dir):
os.makedirs(exp_dir)
export_path = '{}{}_ENV_{}_NB_EP_{}_BS_{}'.format(exp_dir,
args.type,
args.env,
args.nb_episodes,
args.batch_size)
algo.save_weights(export_path)
env.env.close()
def main(args=None):
# Parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# Check if a GPU ID was set
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
set_session(get_session())
summary_writer = tf.summary.FileWriter(args.type + "/tensorboard_" +
args.env)
# Environment Initialization
if (args.is_atari):
# Atari Environment Wrapper
env = AtariEnvironment(args)
state_dim = env.get_state_size()
action_dim = env.get_action_size()
elif (args.type == "DDPG"):
# Continuous Environments Wrapper
env = Environment(gym.make(args.env), args.consecutive_frames)
env.reset()
state_dim = env.get_state_size()
action_space = gym.make(args.env).action_space
action_dim = action_space.high.shape[0]
act_range = action_space.high
else:
if args.env == 'cell':
#do this
env = Environment(opticalTweezers(), args.consecutive_frames)
# env=opticalTweezers(consecutive_frames=args.consecutive_frames)
env.reset()
state_dim = (6, )
action_dim = 4 #note that I have to change the reshape code for a 2d agent # should be 4
else:
# Standard Environments
env = Environment(gym.make(args.env), args.consecutive_frames)
env.reset()
state_dim = env.get_state_size()
print(state_dim)
action_dim = gym.make(args.env).action_space.n
print(action_dim)
# Pick algorithm to train
if (args.type == "DDQN"):
algo = DDQN(action_dim, state_dim, args)
elif (args.type == "A2C"):
algo = A2C(action_dim, state_dim, args.consecutive_frames)
elif (args.type == "A3C"):
algo = A3C(action_dim,
state_dim,
args.consecutive_frames,
is_atari=args.is_atari)
elif (args.type == "DDPG"):
algo = DDPG(action_dim, state_dim, act_range, args.consecutive_frames)
# Train
stats = algo.train(env, args, summary_writer)
# Export results to CSV
if (args.gather_stats):
df = pd.DataFrame(np.array(stats))
df.to_csv(args.type + "/logs.csv",
header=['Episode', 'Mean', 'Stddev'],
float_format='%10.5f')
# Display agent
old_state, time = env.reset(), 0
# all_old_states=[old_state for i in range(args.consecutive_frames)]
while True:
env.render()
a = algo.policy_action(old_state)
old_state, r, done, _ = env.step(a)
time += 1
if done: env.reset()
def main(args=None):
# Parse arguments
if args is None:
args = sys.argv[1:]
args = parse_args(args)
# Check if a GPU ID was set
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
# Environment Initialization
if args.is_ai2thor:
config_dict = {'max_episode_length': 500}
env = AI2ThorEnv(config_dict=config_dict)
env.reset()
state = env.reset()
state_dim = state.shape
action_dim = env.action_space.n
args.env = 'ai2thor'
elif (args.is_atari):
# Atari Environment Wrapper
env = AtariEnvironment(args)
state_dim = env.get_state_size()
action_dim = env.get_action_size()
print(state_dim)
print(action_dim)
else:
# Standard Environments
env = Environment(gym.make(args.env), args.consecutive_frames)
env.reset()
state_dim = env.get_state_size()
action_dim = gym.make(args.env).action_space.n
set_session(get_session())
summary_writer = tf.summary.FileWriter(args.type + "/tensorboard_" +
args.env)
algo = A3C(action_dim,
state_dim,
args.consecutive_frames,
is_atari=args.is_atari,
is_ai2thor=args.is_ai2thor)
# Train
stats = algo.train(env, args, summary_writer)
# Export results to CSV
if args.gather_stats:
df = pd.DataFrame(np.array(stats))
df.to_csv(args.type + "/logs.csv",
header=['Episode', 'Mean', 'Stddev'],
float_format='%10.5f')
# Save weights and close environments
exp_dir = '{}/models/'.format(args.type)
if not os.path.exists(exp_dir):
os.makedirs(exp_dir)
export_path = '{}{}_ENV_{}_NB_EP_{}_BS_{}'.format(exp_dir, args.type,
args.env,
args.nb_episodes,
args.batch_size)
algo.save_weights(export_path)
env.close()