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()
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)
episodic_reward += reward
# call evaluation function - takes in reward received after playing an episode
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()