envs = [wrap_dqn(gym.make(args.env)) for _ in range(ENVS_COUNT)]
test_env = wrap_dqn(gym.make(args.env))
net_act = model.ModelActor(envs[0].observation_space.shape,
envs[0].action_space.n).to(device)
net_crt = model.ModelCritic(envs[0].observation_space.shape).to(device)
print(net_act)
print(net_crt)
writer = SummaryWriter(comment="-acktr_" + args.name)
agent = model.AgentA2C(net_act, device=device)
exp_source = ptan.experience.ExperienceSourceFirstLast(
envs, agent, GAMMA, steps_count=REWARD_STEPS)
opt_act = kfac.KFACOptimizer(net_act, lr=LEARNING_RATE_ACTOR)
opt_crt = optim.Adam(net_crt.parameters(), lr=LEARNING_RATE_CRITIC)
batch = []
best_reward = None
with ptan.common.utils.RewardTracker(writer) as tracker:
with ptan.common.utils.TBMeanTracker(writer,
batch_size=100) as tb_tracker:
for step_idx, exp in enumerate(exp_source):
rewards_steps = exp_source.pop_rewards_steps()
if rewards_steps:
rewards, steps = zip(*rewards_steps)
tb_tracker.track("episode_steps", np.mean(steps), step_idx)
tracker.reward(np.mean(rewards), step_idx)
if step_idx % TEST_ITERS == 0:
action='store_true',
help="Enable Acktr-specific tweaks")
args = parser.parse_args()
get_link_state = rospy.ServiceProxy("/gazebo/get_link_state", GetLinkState)
pitch = 0
rospy.Subscriber('/Bobby/imu', Imu, get_angular_vel)
counter = 0
env = make_env(args)
if args.record:
env = wrappers.Monitor(env, args.record)
net = model.ModelActor(env.observation_space.shape[0],
env.action_space.shape[0], args.hid)
if args.acktr:
opt = kfac.KFACOptimizer(net)
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)
if np.isscalar(action):
action = [action]
obs, reward, done, _ = env.step(action)
total_reward += reward
envs[0].action_space.shape[0]).to(device)
crt_net = model.ModelCritic(envs[0].observation_space.shape[0]).to(device)
print(act_net)
print(crt_net)
if args.act_model:
act_net.load_state_dict(torch.load(args.act_model))
if args.crt_model:
crt_net.load_state_dict(torch.load(args.crt_model))
writer = SummaryWriter(comment='-a2c_' + args.name)
agent = model.AgentA2C(act_net, device)
exp_source = drl.experience.ExperienceSourceFirstLast(
envs, agent, gamma=GAMMA, steps_count=REWARD_STEP)
act_optimizer = kfac.KFACOptimizer(act_net.parameters(),
lr=LEARNING_RATE_ACTOR)
crt_optimizer = optim.Adam(crt_net.parameters(), lr=LEARNING_RATE_CRITIC)
batch = []
best_reward = None
with drl.tracker.RewardTracker(writer) as tracker:
with drl.tracker.TBMeanTracker(writer, 10) as tb_tracker:
for step_idx, exp in enumerate(exp_source):
rewards_steps = exp_source.pop_rewards_steps()
if rewards_steps:
rewards, steps = zip(*rewards_steps)
tb_tracker.track("episode_steps", steps[0], step_idx)
tracker.reward(rewards[0], step_idx)
