def main():
env = KukaGymEnv(renders=True, isDiscrete=False, maxSteps=10000000)
save_path = os.path.join("saves", "ddpg-")
os.makedirs(save_path, exist_ok=True)
device = torch.device("cuda")
act_net = model.DDPGActor(env.observation_space.shape[0],
env.action_space.shape[0]).to(device)
crt_net = model.D4PGCritic(env.observation_space.shape[0],
env.action_space.shape[0], N_ATOMS, Vmin,
Vmax).to(device)
print(act_net)
print(crt_net)
tgt_act_net = common.TargetNet(act_net)
tgt_crt_net = common.TargetNet(crt_net)
writer = SummaryWriter(comment="-d4pg_")
agent = model.AgentDDPG(act_net, device=device)
exp_source = experience.ExperienceSourceFirstLast(env,
agent,
gamma=GAMMA,
steps_count=REWARD_STEPS)
buffer = experience.ExperienceReplayBuffer(exp_source,
buffer_size=REPLAY_SIZE)
act_opt = optim.Adam(act_net.parameters(), lr=LEARNING_RATE)
crt_opt = optim.Adam(crt_net.parameters(), lr=LEARNING_RATE)
frame_idx = 0
best_reward = None
with common.RewardTracker(writer) as tracker:
with common.TBMeanTracker(writer, batch_size=10) as tb_tracker:
while True:
frame_idx += 1
#print("populate")
buffer.populate(1)
rewards_steps = exp_source.pop_rewards_steps()
#print(rewards_steps)
if rewards_steps:
rewards, steps = zip(*rewards_steps)
tb_tracker.track("episode_steps", steps[0], frame_idx)
tracker.reward(rewards[0], frame_idx)
if len(buffer) < 100:
continue
batch = buffer.sample(BATCH_SIZE)
#print("infer")
states_v, actions_v, rewards_v, dones_mask, last_states_v = common.unpack_batch_ddqn(
batch, device)
#print("train critic")# train critic
crt_opt.zero_grad()
crt_distr_v = crt_net(states_v, actions_v)
last_act_v = tgt_act_net.target_model(last_states_v)
last_distr_v = F.softmax(tgt_crt_net.target_model(
last_states_v, last_act_v),
dim=1)
proj_distr_v = distr_projection(last_distr_v,
rewards_v,
dones_mask,
gamma=GAMMA**REWARD_STEPS,
device=device)
prob_dist_v = -F.log_softmax(crt_distr_v, dim=1) * proj_distr_v
critic_loss_v = prob_dist_v.sum(dim=1).mean()
critic_loss_v.backward()
crt_opt.step()
tb_tracker.track("loss_critic", critic_loss_v, frame_idx)
#print("train actor")
# train actor
act_opt.zero_grad()
act_opt.zero_grad()
cur_actions_v = act_net(states_v)
crt_distr_v = crt_net(states_v, cur_actions_v)
actor_loss_v = -crt_net.distr_to_q(crt_distr_v)
actor_loss_v = actor_loss_v.mean()
actor_loss_v.backward()
act_opt.step()
tb_tracker.track("loss_actor", actor_loss_v, frame_idx)
tgt_act_net.alpha_sync(alpha=1 - 1e-3)
tgt_crt_net.alpha_sync(alpha=1 - 1e-3)
if frame_idx % TEST_ITERS == 0:
print("testing")
env.reset()
ts = time.time()
rewards, steps = test_net(act_net, env, device=device)
print("Test done in %.2f sec, reward %.3f, steps %d" %
(time.time() - ts, rewards, steps))
writer.add_scalar("test_reward", rewards, frame_idx)
writer.add_scalar("test_steps", steps, frame_idx)
if best_reward is None or best_reward < rewards:
if best_reward is not None:
print("Best reward updated: %.3f -> %.3f" %
(best_reward, rewards))
name = "best_%+.3f_%d.dat" % (rewards, frame_idx)
fname = os.path.join(save_path, name)
torch.save(act_net.state_dict(), fname)
best_reward = rewards
parser.add_argument("--noisy",
required=False,
action="store_true",
help="Enable noisy network extension")
args = parser.parse_args()
device = torch.device("cuda" if args.cuda else "cpu")
save_path = os.path.join("saves", "ddpg-" + args.name)
os.makedirs(save_path, exist_ok=True)
env = gym.make(ENV_ID)
test_env = gym.make(ENV_ID)
unroll_step = int(args.step) if args.step else 1
act_net = model.DDPGActor(env.observation_space.shape[0],
env.action_space.shape[0], args.noisy).to(device)
crt_net = model.DDPGCritic(env.observation_space.shape[0],
env.action_space.shape[0]).to(device)
print(act_net)
print(crt_net)
tgt_act_net = ptan.agent.TargetNet(act_net)
tgt_crt_net = ptan.agent.TargetNet(crt_net)
writer = SummaryWriter(comment="-ddpg_" + args.name)
agent = model.AgentDDPG(act_net, device=device)
exp_source = ptan.experience.ExperienceSourceFirstLast(
env, agent, gamma=GAMMA, steps_count=unroll_step)
buffer = ptan.experience.ExperienceReplayBuffer(exp_source,
buffer_size=REPLAY_SIZE)
act_opt = optim.Adam(act_net.parameters(), lr=LEARNING_RATE)
crt_opt = optim.Adam(crt_net.parameters(), lr=LEARNING_RATE)
ENV_ID = "MinitaurBulletEnv-v0"
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model", required=True, help="Model file to load")
parser.add_argument("-e", "--env", default=ENV_ID, help="Environment name to use, default=" + ENV_ID)
parser.add_argument("-r", "--record", help="If specified, sets the recording dir, default=Disabled")
args = parser.parse_args()
spec = gym.envs.registry.spec(args.env)
spec._kwargs['render'] = False
env = gym.make(args.env)
if args.record:
env = gym.wrappers.Monitor(env, args.record)
net = model.DDPGActor(env.observation_space.shape[0], env.action_space.shape[0])
net.load_state_dict(torch.load(args.model))
obs = env.reset()
total_reward = 0.0
total_steps = 0
while True:
obs_v = torch.FloatTensor([obs])
mu_v = net(obs_v)
action = mu_v.squeeze(dim=0).data.numpy()
action = np.clip(action, -1, 1)
obs, reward, done, _ = env.step(action)
total_reward += reward
total_steps += 1
if done:
break
rewards += reward
steps += 1
# If done proceed to next try
if is_done:
break
return rewards / count, steps / count
# Create buffer auxiliars
Experience = namedtuple('Episode', field_names=['state', 'action', 'reward', 'last_state', 'done'])
# Initialize simulator
sim = simulator.Agent(random(), random())
# Initialize networks and inteligent agents
act_net = model.DDPGActor(OBSERVATION_SPACE, ACTION_SPACE).to(device)
crt_net = model.DDPGCritic(OBSERVATION_SPACE, ACTION_SPACE).to(device)
tgt_act_net = ptan.agent.TargetNet(act_net)
tgt_crt_net = ptan.agent.TargetNet(crt_net)
agent = model.AgentDDPG(act_net, device=device)
act_opt = optim.Adam(act_net.parameters(), lr=LEARNING_RATE)
crt_opt = optim.Adam(crt_net.parameters(), lr=LEARNING_RATE)
# Define soft_max function for discrete actions
def softmax_function(values):
return_values = [max(MIN_PROB_EXPLORATION, np.exp(value)/np.exp(values).sum()) for value in values]
return np.random.choice(len(return_values), p=return_values/sum(return_values))
buffer = []
