envs = [gym.make(args.env) for _ in range(ENVS_COUNT)]
test_env = gym.make(ENV_ID)
act_net = model.ModelActor(envs[0].observation_space.shape[0],
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 = optim.Adam(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()
device = torch.device("cuda" if args.cuda else "cpu")
save_path = os.path.join("saves", "trpo-" + args.name)
os.makedirs(save_path, exist_ok=True)
env = gym.make(args.env)
test_env = gym.make(args.env)
net_act = model.ModelActor(env.observation_space.shape[0],
env.action_space.shape[0]).to(device)
net_crt = model.ModelCritic(env.observation_space.shape[0]).to(device)
print(net_act)
print(net_crt)
writer = SummaryWriter(comment="-trpo_" + args.name)
agent = model.AgentA2C(net_act, device=device)
exp_source = ptan.experience.ExperienceSource(env, agent, steps_count=1)
opt_crt = optim.Adam(net_crt.parameters(), lr=LEARNING_RATE_CRITIC)
trajectory = []
best_reward = None
with ptan.common.utils.RewardTracker(writer) as tracker:
for step_idx, exp in enumerate(exp_source):
rewards_steps = exp_source.pop_rewards_steps()
if rewards_steps:
rewards, steps = zip(*rewards_steps)
writer.add_scalar("episode_steps", np.mean(steps), step_idx)
tracker.reward(np.mean(rewards), step_idx)
if step_idx % TEST_ITERS == 0:
os.makedirs(save_path, exist_ok=True)
env = gym.make(args.env)
test_env = gym.make(args.env)
net_act = model.ModelActor(env.observation_space.shape[0],
env.action_space.shape[0])
net_crt = model.ModelCritic(env.observation_space.shape[0])
if args.cuda:
net_act.cuda()
net_crt.cuda()
print(net_act)
print(net_crt)
writer = SummaryWriter(comment="-trpo_" + args.name)
agent = model.AgentA2C(net_act, cuda=args.cuda)
exp_source = ptan.experience.ExperienceSource(env, agent, steps_count=1)
opt_crt = optim.Adam(net_crt.parameters(), lr=LEARNING_RATE_CRITIC)
trajectory = []
best_reward = None
with ptan.common.utils.RewardTracker(writer) as tracker:
for step_idx, exp in enumerate(exp_source):
rewards_steps = exp_source.pop_rewards_steps()
if rewards_steps:
rewards, steps = zip(*rewards_steps)
writer.add_scalar("episode_steps", np.mean(steps), step_idx)
tracker.reward(np.mean(rewards), step_idx)
if step_idx % TEST_ITERS == 0:
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--cuda",
default=False,
action='store_true',
help='Enable CUDA')
parser.add_argument("-n", "--name", required=True, help="Name of the run")
parser.add_argument("-e",
"--env",
default=ENV_ID,
help="Environment id, default=" + ENV_ID)
parser.add_argument("--lrc",
default=LEARNING_RATE_CRITIC,
type=float,
help="Critic learning rate")
parser.add_argument("--lra",
default=LEARNING_RATE_ACTOR,
type=float,
help="Actor learning rate")
args = parser.parse_args()
device = torch.device("cuda" if args.cuda else "cpu")
save_path = os.path.join("saves", "ppo-" + args.name)
os.makedirs(save_path, exist_ok=True)
env = gym.make(args.env)
test_env = gym.make(args.env)
net_act = model.ModelActor(env.observation_space.shape[0],
env.action_space.shape[0]).to(device)
net_crt = model.ModelCritic(env.observation_space.shape[0]).to(device)
print(net_act)
print(net_crt)
writer = SummaryWriter(comment="-ppo_" + args.name)
agent = model.AgentA2C(net_act, device=device)
exp_source = ptan.experience.ExperienceSource(env, agent, steps_count=1)
opt_act = optim.Adam(net_act.parameters(), lr=args.lra)
opt_crt = optim.Adam(net_crt.parameters(), lr=args.lrc)
trajectory = []
best_reward = None
with ptan.common.utils.RewardTracker(writer) as tracker:
for step_idx, exp in enumerate(exp_source):
rewards_steps = exp_source.pop_rewards_steps()
if rewards_steps:
rewards, steps = zip(*rewards_steps)
writer.add_scalar("episode_steps", np.mean(steps), step_idx)
tracker.reward(np.mean(rewards), step_idx)
if step_idx % TEST_ITERS == 0:
ts = time.time()
rewards, steps = test_net(net_act, test_env, device=device)
print("Test done in %.2f sec, reward %.3f, steps %d" %
(time.time() - ts, rewards, steps))
writer.add_scalar("test_reward", rewards, step_idx)
writer.add_scalar("test_steps", steps, step_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, step_idx)
fname = os.path.join(save_path, name)
torch.save(net_act.state_dict(), fname)
best_reward = rewards
trajectory.append(exp)
if len(trajectory) < TRAJECTORY_SIZE:
continue
traj_states = [t[0].state for t in trajectory]
traj_actions = [t[0].action for t in trajectory]
traj_states_v = torch.FloatTensor(traj_states)
traj_states_v = traj_states_v.to(device)
traj_actions_v = torch.FloatTensor(traj_actions)
traj_actions_v = traj_actions_v.to(device)
traj_adv_v, traj_ref_v = calc_adv_ref(trajectory,
net_crt,
traj_states_v,
device=device)
mu_v = net_act(traj_states_v)
old_logprob_v = calc_logprob(mu_v, net_act.logstd, traj_actions_v)
# normalize advantages
traj_adv_v = traj_adv_v - torch.mean(traj_adv_v)
traj_adv_v /= torch.std(traj_adv_v)
# drop last entry from the trajectory, an our adv and ref value calculated without it
trajectory = trajectory[:-1]
old_logprob_v = old_logprob_v[:-1].detach()
sum_loss_value = 0.0
sum_loss_policy = 0.0
count_steps = 0
for epoch in range(PPO_EPOCHES):
for batch_ofs in range(0, len(trajectory), PPO_BATCH_SIZE):
batch_l = batch_ofs + PPO_BATCH_SIZE
states_v = traj_states_v[batch_ofs:batch_l]
actions_v = traj_actions_v[batch_ofs:batch_l]
batch_adv_v = traj_adv_v[batch_ofs:batch_l]
batch_adv_v = batch_adv_v.unsqueeze(-1)
batch_ref_v = traj_ref_v[batch_ofs:batch_l]
batch_old_logprob_v = \
old_logprob_v[batch_ofs:batch_l]
# critic training
opt_crt.zero_grad()
value_v = net_crt(states_v)
loss_value_v = F.mse_loss(value_v.squeeze(-1), batch_ref_v)
loss_value_v.backward()
opt_crt.step()
# actor training
opt_act.zero_grad()
mu_v = net_act(states_v)
logprob_pi_v = calc_logprob(mu_v, net_act.logstd,
actions_v)
ratio_v = torch.exp(logprob_pi_v - batch_old_logprob_v)
surr_obj_v = batch_adv_v * ratio_v
c_ratio_v = torch.clamp(ratio_v, 1.0 - PPO_EPS,
1.0 + PPO_EPS)
clipped_surr_v = batch_adv_v * c_ratio_v
loss_policy_v = -torch.min(surr_obj_v,
clipped_surr_v).mean()
loss_policy_v.backward()
opt_act.step()
sum_loss_value += loss_value_v.item()
sum_loss_policy += loss_policy_v.item()
count_steps += 1
trajectory.clear()
writer.add_scalar("advantage", traj_adv_v.mean().item(), step_idx)
writer.add_scalar("values", traj_ref_v.mean().item(), step_idx)
writer.add_scalar("loss_policy", sum_loss_policy / count_steps,
step_idx)
writer.add_scalar("loss_value", sum_loss_value / count_steps,
step_idx)
parser.add_argument("-n", "--name", required=True, help="Name of the run")
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
save_path = os.path.join("saves", "a2c-" + args.name)
os.makedirs(save_path, exist_ok=True)
env = gym.make(ENV_ID)
test_env = gym.make(ENV_ID)
writer = SummaryWriter(comment="-a2c_" + args.name)
net = model.ModelA2C(env.observation_space.shape[0],
env.action_space.shape[0]).to(device)
print(net)
agent = model.AgentA2C(device=device, net=net)
exp_source = ptan.experience.ExperienceSourceFirstLast(
env, agent, GAMMA, steps_count=REWARD_STEPS)
optimizer = optim.Adam(net.parameters(), lr=LEARNING_RATE)
batch = []
best_reward = None
with ptan.common.utils.RewardTracker(writer) as tracker:
with ptan.common.utils.TBMeanTracker(writer,
batch_size=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)
