# env = UnityEnvironment(file_name="Reacher_Linux/Reacher.x86_64", no_graphics=args.no_graphics)
env = UnityEnvironment(file_name="Reacher.app",
no_graphics=args.no_graphics)
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
env_info = env.reset(train_mode=False)[brain_name]
action_size = brain.vector_action_space_size
state = env_info.vector_observations[0]
reward = env_info.rewards[0]
action = 0
state_size = len(state)
#
b_agent = Agent(args.model_name, state_size, action_size)
try:
b_agent.load() # try to load to continue training
except:
pass
for epx in range(1, args.episodes + 1):
at_step = 0
env_info = env.reset(train_mode=False)[brain_name]
b_agent.reset_episode()
while True:
action = b_agent.act(state)
env_info = env.step(action)[brain_name]
at_step += 1
next_state = env_info.vector_observations[0]
reward = env_info.rewards[0]
done = env_info.local_done[0]
if at_step % 100 == 0:
args = parser.parse_args()
is_training = args.mode == 'training'
env = RLBenchEnv("ReachTarget", state_type_list=STATE_TYPE_LIST)
state = env.reset()
action_dim = env.action_space.shape[0]
state_space = env.observation_space
agent = Agent(state_space, HIDDEN_SIZE, action_dim, 1,
seed=SEED, buffer_size=MEMORY_BUFFER_SIZE,
actor_lr=ACTOR_LR, actor_hidden_sizes=ACTOR_HIDDEN_UNITS, actor_weight_decay=ACTOR_WEIGHT_DECAY,
critic_lr=CRITIC_LR, critic_hidden_sizes=CRITIC_HIDDEN_UNITS, critic_weight_decay=CRITIC_WEIGHT_DECAY,
batch_size=BATCH_SIZE, gamma=GAMMA, tau=TAU
)
print(agent)
agent.load()
scores, actor_losses, critic_losses = run_ddpg(n_episodes=N_EPISODES, is_training=is_training,
eps_start=EPS_START if is_training else EPS_END,
eps_decay=EPS_DECAY, eps_end=EPS_END,
max_t=MAX_STEPS, learn_every_step=LEARN_EVERY_STEP)
if is_training:
agent.save()
fig = plt.figure()
ax1 = fig.add_subplot(311)
ax1.plot(np.arange(1, len(scores) + 1), scores)
ax1.set_ylabel('Score')
ax1.set_xlabel('Episode #')
