Advanced Actor-Critic Methods
그로킹 심층 강화학습 중 12장 내용인 "조금 더 발전된 액터-크리틱 학습 방법들"에 대한 내용입니다.
Note: 실행을 위해 아래의 패키지들을 설치해주기 바랍니다.
!pip install tqdm numpy scikit-learn pyglet setuptools
!pip install gym asciinema pandas tabulate tornado==5.* PyBullet
!pip install git+https://github.com/pybox2d/pybox2d#egg=Box2D
!pip install git+https://github.com/mimoralea/gym-bandits#egg=gym-bandits
!pip install git+https://github.com/mimoralea/gym-walk#egg=gym-walk
!pip install git+https://github.com/mimoralea/gym-aima#egg=gym-aima
!pip install gym[atari]
!pip install torch torchvision
import warnings ; warnings.filterwarnings('ignore')
import os
os.environ['CUDA_DEVICE_ORDER']='PCI_BUS_ID'
os.environ['OMP_NUM_THREADS'] = '1'
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.multiprocessing as mp
import threading
from torch.distributions import Normal
import numpy as np
from IPython.display import display
from collections import namedtuple, deque
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from itertools import cycle, count
from textwrap import wrap
import pybullet_envs
import matplotlib
import subprocess
import os.path
import tempfile
import random
import base64
import pprint
import glob
import time
import json
import sys
import gym
import io
import os
import gc
import platform
from gym import wrappers
from skimage.transform import resize
from skimage.color import rgb2gray
from subprocess import check_output
from IPython.display import display, HTML
LEAVE_PRINT_EVERY_N_SECS = 300
ERASE_LINE = '\x1b[2K'
EPS = 1e-6
RESULTS_DIR = os.path.join('.', 'gym-results')
SEEDS = (12, 34, 56, 78, 90)
%matplotlib inline
plt.style.use('fivethirtyeight')
params = {
'figure.figsize': (15, 8),
'font.size': 24,
'legend.fontsize': 20,
'axes.titlesize': 28,
'axes.labelsize': 24,
'xtick.labelsize': 20,
'ytick.labelsize': 20
}
pylab.rcParams.update(params)
np.set_printoptions(suppress=True)
torch.cuda.is_available()
def get_make_env_fn(**kargs):
def make_env_fn(env_name, seed=None, render=None, record=False,
unwrapped=False, monitor_mode=None,
inner_wrappers=None, outer_wrappers=None):
mdir = tempfile.mkdtemp()
env = None
if render:
try:
env = gym.make(env_name, render=render)
except:
pass
if env is None:
env = gym.make(env_name)
if seed is not None: env.seed(seed)
env = env.unwrapped if unwrapped else env
if inner_wrappers:
for wrapper in inner_wrappers:
env = wrapper(env)
env = wrappers.Monitor(
env, mdir, force=True,
mode=monitor_mode,
video_callable=lambda e_idx: record) if monitor_mode else env
if outer_wrappers:
for wrapper in outer_wrappers:
env = wrapper(env)
return env
return make_env_fn, kargs
def get_videos_html(env_videos, title, max_n_videos=5):
videos = np.array(env_videos)
if len(videos) == 0:
return
n_videos = max(1, min(max_n_videos, len(videos)))
idxs = np.linspace(0, len(videos) - 1, n_videos).astype(int) if n_videos > 1 else [-1,]
videos = videos[idxs,...]
strm = '<h2>{}</h2>'.format(title)
for video_path, meta_path in videos:
video = io.open(video_path, 'r+b').read()
encoded = base64.b64encode(video)
with open(meta_path) as data_file:
meta = json.load(data_file)
html_tag = """
<h3>{0}</h3>
<video width="960" height="540" controls>
<source src="data:video/mp4;base64,{1}" type="video/mp4" />
</video>"""
strm += html_tag.format('Episode ' + str(meta['episode_id']), encoded.decode('ascii'))
return strm
platform.system()
def get_gif_html(env_videos, title, subtitle_eps=None, max_n_videos=4):
videos = np.array(env_videos)
if len(videos) == 0:
return
n_videos = max(1, min(max_n_videos, len(videos)))
idxs = np.linspace(0, len(videos) - 1, n_videos).astype(int) if n_videos > 1 else [-1,]
videos = videos[idxs,...]
strm = '<h2>{}</h2>'.format(title)
for video_path, meta_path in videos:
basename = os.path.splitext(video_path)[0]
gif_path = basename + '.gif'
if not os.path.exists(gif_path):
if platform.system() == 'Linux':
ps = subprocess.Popen(
('ffmpeg',
'-i', video_path,
'-r', '7',
'-f', 'image2pipe',
'-vcodec', 'ppm',
'-crf', '20',
'-vf', 'scale=512:-1',
'-'),
stdout=subprocess.PIPE,
universal_newlines=True)
output = subprocess.check_output(
('convert',
'-coalesce',
'-delay', '7',
'-loop', '0',
'-fuzz', '2%',
'+dither',
'-deconstruct',
'-layers', 'Optimize',
'-', gif_path),
stdin=ps.stdout)
ps.wait()
else:
ps = subprocess.Popen('ffmpeg -i {} -r 7 -f image2pipe \
-vcodec ppm -crf 20 -vf scale=512:-1 - | \
convert -coalesce -delay 7 -loop 0 -fuzz 2% \
+dither -deconstruct -layers Optimize \
- {}'.format(video_path, gif_path),
stdin=subprocess.PIPE,
shell=True)
ps.wait()
gif = io.open(gif_path, 'r+b').read()
encoded = base64.b64encode(gif)
with open(meta_path) as data_file:
meta = json.load(data_file)
html_tag = """
<h3>{0}</h3>
<img src="data:image/gif;base64,{1}" />"""
prefix = 'Trial ' if subtitle_eps is None else 'Episode '
sufix = str(meta['episode_id'] if subtitle_eps is None \
else subtitle_eps[meta['episode_id']])
strm += html_tag.format(prefix + sufix, encoded.decode('ascii'))
return strm
class RenderUint8(gym.Wrapper):
def __init__(self, env):
super().__init__(env)
def reset(self, **kwargs):
return self.env.reset(**kwargs)
def render(self, mode='rgb_array'):
frame = self.env.render(mode=mode)
return frame.astype(np.uint8)
class FCQV(nn.Module):
def __init__(self,
input_dim,
output_dim,
hidden_dims=(32,32),
activation_fc=F.relu):
super(FCQV, self).__init__()
self.activation_fc = activation_fc
self.input_layer = nn.Linear(input_dim, hidden_dims[0])
self.hidden_layers = nn.ModuleList()
for i in range(len(hidden_dims)-1):
in_dim = hidden_dims[i]
if i == 0:
in_dim += output_dim
hidden_layer = nn.Linear(in_dim, hidden_dims[i+1])
self.hidden_layers.append(hidden_layer)
self.output_layer = nn.Linear(hidden_dims[-1], 1)
device = "cpu"
if torch.cuda.is_available():
device = "cuda:0"
self.device = torch.device(device)
self.to(self.device)
def _format(self, state, action):
x, u = state, action
if not isinstance(x, torch.Tensor):
x = torch.tensor(x,
device=self.device,
dtype=torch.float32)
x = x.unsqueeze(0)
if not isinstance(u, torch.Tensor):
u = torch.tensor(u,
device=self.device,
dtype=torch.float32)
u = u.unsqueeze(0)
return x, u
def forward(self, state, action):
x, u = self._format(state, action)
x = self.activation_fc(self.input_layer(x))
for i, hidden_layer in enumerate(self.hidden_layers):
if i == 0:
x = torch.cat((x, u), dim=1)
x = self.activation_fc(hidden_layer(x))
return self.output_layer(x)
def load(self, experiences):
states, actions, new_states, rewards, is_terminals = experiences
states = torch.from_numpy(states).float().to(self.device)
actions = torch.from_numpy(actions).float().to(self.device)
new_states = torch.from_numpy(new_states).float().to(self.device)
rewards = torch.from_numpy(rewards).float().to(self.device)
is_terminals = torch.from_numpy(is_terminals).float().to(self.device)
return states, actions, new_states, rewards, is_terminals
class FCDP(nn.Module):
def __init__(self,
input_dim,
action_bounds,
hidden_dims=(32,32),
activation_fc=F.relu,
out_activation_fc=F.tanh):
super(FCDP, self).__init__()
self.activation_fc = activation_fc
self.out_activation_fc = out_activation_fc
self.env_min, self.env_max = action_bounds
self.input_layer = nn.Linear(input_dim, hidden_dims[0])
self.hidden_layers = nn.ModuleList()
for i in range(len(hidden_dims)-1):
hidden_layer = nn.Linear(hidden_dims[i], hidden_dims[i+1])
self.hidden_layers.append(hidden_layer)
self.output_layer = nn.Linear(hidden_dims[-1], len(self.env_max))
device = "cpu"
if torch.cuda.is_available():
device = "cuda:0"
self.device = torch.device(device)
self.to(self.device)
self.env_min = torch.tensor(self.env_min,
device=self.device,
dtype=torch.float32)
self.env_max = torch.tensor(self.env_max,
device=self.device,
dtype=torch.float32)
self.nn_min = self.out_activation_fc(
torch.Tensor([float('-inf')])).to(self.device)
self.nn_max = self.out_activation_fc(
torch.Tensor([float('inf')])).to(self.device)
self.rescale_fn = lambda x: (x - self.nn_min) * (self.env_max - self.env_min) / \
(self.nn_max - self.nn_min) + self.env_min
def _format(self, state):
x = state
if not isinstance(x, torch.Tensor):
x = torch.tensor(x,
device=self.device,
dtype=torch.float32)
x = x.unsqueeze(0)
return x
def forward(self, state):
x = self._format(state)
x = self.activation_fc(self.input_layer(x))
for hidden_layer in self.hidden_layers:
x = self.activation_fc(hidden_layer(x))
x = self.output_layer(x)
x = self.out_activation_fc(x)
return self.rescale_fn(x)
class ReplayBuffer():
def __init__(self,
max_size=10000,
batch_size=64):
self.ss_mem = np.empty(shape=(max_size), dtype=np.ndarray)
self.as_mem = np.empty(shape=(max_size), dtype=np.ndarray)
self.rs_mem = np.empty(shape=(max_size), dtype=np.ndarray)
self.ps_mem = np.empty(shape=(max_size), dtype=np.ndarray)
self.ds_mem = np.empty(shape=(max_size), dtype=np.ndarray)
self.max_size = max_size
self.batch_size = batch_size
self._idx = 0
self.size = 0
def store(self, sample):
s, a, r, p, d = sample
self.ss_mem[self._idx] = s
self.as_mem[self._idx] = a
self.rs_mem[self._idx] = r
self.ps_mem[self._idx] = p
self.ds_mem[self._idx] = d
self._idx += 1
self._idx = self._idx % self.max_size
self.size += 1
self.size = min(self.size, self.max_size)
def sample(self, batch_size=None):
if batch_size == None:
batch_size = self.batch_size
idxs = np.random.choice(
self.size, batch_size, replace=False)
experiences = np.vstack(self.ss_mem[idxs]), \
np.vstack(self.as_mem[idxs]), \
np.vstack(self.rs_mem[idxs]), \
np.vstack(self.ps_mem[idxs]), \
np.vstack(self.ds_mem[idxs])
return experiences
def __len__(self):
return self.size
class GreedyStrategy():
def __init__(self, bounds):
self.low, self.high = bounds
self.ratio_noise_injected = 0
def select_action(self, model, state):
with torch.no_grad():
greedy_action = model(state).cpu().detach().data.numpy().squeeze()
action = np.clip(greedy_action, self.low, self.high)
return np.reshape(action, self.high.shape)
class NormalNoiseStrategy():
def __init__(self, bounds, exploration_noise_ratio=0.1):
self.low, self.high = bounds
self.exploration_noise_ratio = exploration_noise_ratio
self.ratio_noise_injected = 0
def select_action(self, model, state, max_exploration=False):
if max_exploration:
noise_scale = self.high
else:
noise_scale = self.exploration_noise_ratio * self.high
with torch.no_grad():
greedy_action = model(state).cpu().detach().data.numpy().squeeze()
noise = np.random.normal(loc=0, scale=noise_scale, size=len(self.high))
noisy_action = greedy_action + noise
action = np.clip(noisy_action, self.low, self.high)
self.ratio_noise_injected = np.mean(abs((greedy_action - action)/(self.high - self.low)))
return action
class DDPG():
def __init__(self,
replay_buffer_fn,
policy_model_fn,
policy_max_grad_norm,
policy_optimizer_fn,
policy_optimizer_lr,
value_model_fn,
value_max_grad_norm,
value_optimizer_fn,
value_optimizer_lr,
training_strategy_fn,
evaluation_strategy_fn,
n_warmup_batches,
update_target_every_steps,
tau):
self.replay_buffer_fn = replay_buffer_fn
self.policy_model_fn = policy_model_fn
self.policy_max_grad_norm = policy_max_grad_norm
self.policy_optimizer_fn = policy_optimizer_fn
self.policy_optimizer_lr = policy_optimizer_lr
self.value_model_fn = value_model_fn
self.value_max_grad_norm = value_max_grad_norm
self.value_optimizer_fn = value_optimizer_fn
self.value_optimizer_lr = value_optimizer_lr
self.training_strategy_fn = training_strategy_fn
self.evaluation_strategy_fn = evaluation_strategy_fn
self.n_warmup_batches = n_warmup_batches
self.update_target_every_steps = update_target_every_steps
self.tau = tau
def optimize_model(self, experiences):
states, actions, rewards, next_states, is_terminals = experiences
batch_size = len(is_terminals)
argmax_a_q_sp = self.target_policy_model(next_states)
max_a_q_sp = self.target_value_model(next_states, argmax_a_q_sp)
target_q_sa = rewards + self.gamma * max_a_q_sp * (1 - is_terminals)
q_sa = self.online_value_model(states, actions)
td_error = q_sa - target_q_sa.detach()
value_loss = td_error.pow(2).mul(0.5).mean()
self.value_optimizer.zero_grad()
value_loss.backward()
torch.nn.utils.clip_grad_norm_(self.online_value_model.parameters(),
self.value_max_grad_norm)
self.value_optimizer.step()
argmax_a_q_s = self.online_policy_model(states)
max_a_q_s = self.online_value_model(states, argmax_a_q_s)
policy_loss = -max_a_q_s.mean()
self.policy_optimizer.zero_grad()
policy_loss.backward()
torch.nn.utils.clip_grad_norm_(self.online_policy_model.parameters(),
self.policy_max_grad_norm)
self.policy_optimizer.step()
def interaction_step(self, state, env):
min_samples = self.replay_buffer.batch_size * self.n_warmup_batches
action = self.training_strategy.select_action(self.online_policy_model,
state,
len(self.replay_buffer) < min_samples)
new_state, reward, is_terminal, info = env.step(action)
is_truncated = 'TimeLimit.truncated' in info and info['TimeLimit.truncated']
is_failure = is_terminal and not is_truncated
experience = (state, action, reward, new_state, float(is_failure))
self.replay_buffer.store(experience)
self.episode_reward[-1] += reward
self.episode_timestep[-1] += 1
self.episode_exploration[-1] += self.training_strategy.ratio_noise_injected
return new_state, is_terminal
def update_networks(self, tau=None):
tau = self.tau if tau is None else tau
for target, online in zip(self.target_value_model.parameters(),
self.online_value_model.parameters()):
target_ratio = (1.0 - self.tau) * target.data
online_ratio = self.tau * online.data
mixed_weights = target_ratio + online_ratio
target.data.copy_(mixed_weights)
for target, online in zip(self.target_policy_model.parameters(),
self.online_policy_model.parameters()):
target_ratio = (1.0 - self.tau) * target.data
online_ratio = self.tau * online.data
mixed_weights = target_ratio + online_ratio
target.data.copy_(mixed_weights)
def train(self, make_env_fn, make_env_kargs, seed, gamma,
max_minutes, max_episodes, goal_mean_100_reward):
training_start, last_debug_time = time.time(), float('-inf')
self.checkpoint_dir = tempfile.mkdtemp()
self.make_env_fn = make_env_fn
self.make_env_kargs = make_env_kargs
self.seed = seed
self.gamma = gamma
env = self.make_env_fn(**self.make_env_kargs, seed=self.seed)
torch.manual_seed(self.seed) ; np.random.seed(self.seed) ; random.seed(self.seed)
nS, nA = env.observation_space.shape[0], env.action_space.shape[0]
action_bounds = env.action_space.low, env.action_space.high
self.episode_timestep = []
self.episode_reward = []
self.episode_seconds = []
self.evaluation_scores = []
self.episode_exploration = []
self.target_value_model = self.value_model_fn(nS, nA)
self.online_value_model = self.value_model_fn(nS, nA)
self.target_policy_model = self.policy_model_fn(nS, action_bounds)
self.online_policy_model = self.policy_model_fn(nS, action_bounds)
self.update_networks(tau=1.0)
self.value_optimizer = self.value_optimizer_fn(self.online_value_model,
self.value_optimizer_lr)
self.policy_optimizer = self.policy_optimizer_fn(self.online_policy_model,
self.policy_optimizer_lr)
self.replay_buffer = self.replay_buffer_fn()
self.training_strategy = training_strategy_fn(action_bounds)
self.evaluation_strategy = evaluation_strategy_fn(action_bounds)
result = np.empty((max_episodes, 5))
result[:] = np.nan
training_time = 0
for episode in range(1, max_episodes + 1):
episode_start = time.time()
state, is_terminal = env.reset(), False
self.episode_reward.append(0.0)
self.episode_timestep.append(0.0)
self.episode_exploration.append(0.0)
for step in count():
state, is_terminal = self.interaction_step(state, env)
min_samples = self.replay_buffer.batch_size * self.n_warmup_batches
if len(self.replay_buffer) > min_samples:
experiences = self.replay_buffer.sample()
experiences = self.online_value_model.load(experiences)
self.optimize_model(experiences)
if np.sum(self.episode_timestep) % self.update_target_every_steps == 0:
self.update_networks()
if is_terminal:
gc.collect()
break
# stats
episode_elapsed = time.time() - episode_start
self.episode_seconds.append(episode_elapsed)
training_time += episode_elapsed
evaluation_score, _ = self.evaluate(self.online_policy_model, env)
self.save_checkpoint(episode-1, self.online_policy_model)
total_step = int(np.sum(self.episode_timestep))
self.evaluation_scores.append(evaluation_score)
mean_10_reward = np.mean(self.episode_reward[-10:])
std_10_reward = np.std(self.episode_reward[-10:])
mean_100_reward = np.mean(self.episode_reward[-100:])
std_100_reward = np.std(self.episode_reward[-100:])
mean_100_eval_score = np.mean(self.evaluation_scores[-100:])
std_100_eval_score = np.std(self.evaluation_scores[-100:])
lst_100_exp_rat = np.array(
self.episode_exploration[-100:])/np.array(self.episode_timestep[-100:])
mean_100_exp_rat = np.mean(lst_100_exp_rat)
std_100_exp_rat = np.std(lst_100_exp_rat)
wallclock_elapsed = time.time() - training_start
result[episode-1] = total_step, mean_100_reward, \
mean_100_eval_score, training_time, wallclock_elapsed
reached_debug_time = time.time() - last_debug_time >= LEAVE_PRINT_EVERY_N_SECS
reached_max_minutes = wallclock_elapsed >= max_minutes * 60
reached_max_episodes = episode >= max_episodes
reached_goal_mean_reward = mean_100_eval_score >= goal_mean_100_reward
training_is_over = reached_max_minutes or \
reached_max_episodes or \
reached_goal_mean_reward
elapsed_str = time.strftime("%H:%M:%S", time.gmtime(time.time() - training_start))
debug_message = 'el {}, ep {:04}, ts {:07}, '
debug_message += 'ar 10 {:05.1f}\u00B1{:05.1f}, '
debug_message += '100 {:05.1f}\u00B1{:05.1f}, '
debug_message += 'ex 100 {:02.1f}\u00B1{:02.1f}, '
debug_message += 'ev {:05.1f}\u00B1{:05.1f}'
debug_message = debug_message.format(
elapsed_str, episode-1, total_step, mean_10_reward, std_10_reward,
mean_100_reward, std_100_reward, mean_100_exp_rat, std_100_exp_rat,
mean_100_eval_score, std_100_eval_score)
print(debug_message, end='\r', flush=True)
if reached_debug_time or training_is_over:
print(ERASE_LINE + debug_message, flush=True)
last_debug_time = time.time()
if training_is_over:
if reached_max_minutes: print(u'--> reached_max_minutes \u2715')
if reached_max_episodes: print(u'--> reached_max_episodes \u2715')
if reached_goal_mean_reward: print(u'--> reached_goal_mean_reward \u2713')
break
final_eval_score, score_std = self.evaluate(self.online_policy_model, env, n_episodes=100)
wallclock_time = time.time() - training_start
print('Training complete.')
print('Final evaluation score {:.2f}\u00B1{:.2f} in {:.2f}s training time,'
' {:.2f}s wall-clock time.\n'.format(
final_eval_score, score_std, training_time, wallclock_time))
env.close() ; del env
self.get_cleaned_checkpoints()
return result, final_eval_score, training_time, wallclock_time
def evaluate(self, eval_policy_model, eval_env, n_episodes=1):
rs = []
for _ in range(n_episodes):
s, d = eval_env.reset(), False
rs.append(0)
for _ in count():
a = self.evaluation_strategy.select_action(eval_policy_model, s)
s, r, d, _ = eval_env.step(a)
rs[-1] += r
if d: break
return np.mean(rs), np.std(rs)
def get_cleaned_checkpoints(self, n_checkpoints=4):
try:
return self.checkpoint_paths
except AttributeError:
self.checkpoint_paths = {}
paths = glob.glob(os.path.join(self.checkpoint_dir, '*.tar'))
paths_dic = {int(path.split('.')[-2]):path for path in paths}
last_ep = max(paths_dic.keys())
# checkpoint_idxs = np.geomspace(1, last_ep+1, n_checkpoints, endpoint=True, dtype=np.int)-1
checkpoint_idxs = np.linspace(1, last_ep+1, n_checkpoints, endpoint=True, dtype=np.int)-1
for idx, path in paths_dic.items():
if idx in checkpoint_idxs:
self.checkpoint_paths[idx] = path
else:
os.unlink(path)
return self.checkpoint_paths
def demo_last(self, title='Fully-trained {} Agent', n_episodes=2, max_n_videos=2):
env = self.make_env_fn(**self.make_env_kargs, monitor_mode='evaluation', render=True, record=True)
checkpoint_paths = self.get_cleaned_checkpoints()
last_ep = max(checkpoint_paths.keys())
self.online_policy_model.load_state_dict(torch.load(checkpoint_paths[last_ep]))
self.evaluate(self.online_policy_model, env, n_episodes=n_episodes)
env.close()
data = get_gif_html(env_videos=env.videos,
title=title.format(self.__class__.__name__),
max_n_videos=max_n_videos)
del env
return HTML(data=data)
def demo_progression(self, title='{} Agent progression', max_n_videos=4):
env = self.make_env_fn(**self.make_env_kargs, monitor_mode='evaluation', render=True, record=True)
checkpoint_paths = self.get_cleaned_checkpoints()
for i in sorted(checkpoint_paths.keys()):
self.online_policy_model.load_state_dict(torch.load(checkpoint_paths[i]))
self.evaluate(self.online_policy_model, env, n_episodes=1)
env.close()
data = get_gif_html(env_videos=env.videos,
title=title.format(self.__class__.__name__),
subtitle_eps=sorted(checkpoint_paths.keys()),
max_n_videos=max_n_videos)
del env
return HTML(data=data)
def save_checkpoint(self, episode_idx, model):
torch.save(model.state_dict(),
os.path.join(self.checkpoint_dir, 'model.{}.tar'.format(episode_idx)))
ddpg_results = []
best_agent, best_eval_score = None, float('-inf')
for seed in SEEDS:
environment_settings = {
'env_name': 'Pendulum-v0',
'gamma': 0.99,
'max_minutes': 20,
'max_episodes': 500,
'goal_mean_100_reward': -150
}
policy_model_fn = lambda nS, bounds: FCDP(nS, bounds, hidden_dims=(256,256))
policy_max_grad_norm = float('inf')
policy_optimizer_fn = lambda net, lr: optim.Adam(net.parameters(), lr=lr)
policy_optimizer_lr = 0.0003
value_model_fn = lambda nS, nA: FCQV(nS, nA, hidden_dims=(256,256))
value_max_grad_norm = float('inf')
value_optimizer_fn = lambda net, lr: optim.Adam(net.parameters(), lr=lr)
value_optimizer_lr = 0.0003
training_strategy_fn = lambda bounds: NormalNoiseStrategy(bounds, exploration_noise_ratio=0.1)
evaluation_strategy_fn = lambda bounds: GreedyStrategy(bounds)
replay_buffer_fn = lambda: ReplayBuffer(max_size=100000, batch_size=256)
n_warmup_batches = 5
update_target_every_steps = 1
tau = 0.005
env_name, gamma, max_minutes, \
max_episodes, goal_mean_100_reward = environment_settings.values()
agent = DDPG(replay_buffer_fn,
policy_model_fn,
policy_max_grad_norm,
policy_optimizer_fn,
policy_optimizer_lr,
value_model_fn,
value_max_grad_norm,
value_optimizer_fn,
value_optimizer_lr,
training_strategy_fn,
evaluation_strategy_fn,
n_warmup_batches,
update_target_every_steps,
tau)
make_env_fn, make_env_kargs = get_make_env_fn(env_name=env_name)
result, final_eval_score, training_time, wallclock_time = agent.train(
make_env_fn, make_env_kargs, seed, gamma, max_minutes, max_episodes, goal_mean_100_reward)
ddpg_results.append(result)
if final_eval_score > best_eval_score:
best_eval_score = final_eval_score
best_agent = agent
ddpg_results = np.array(ddpg_results)
best_agent.demo_progression()
DDPG Agent progression
Episode 0
Episode 38
Episode 77
Episode 116
best_agent.demo_last()
Fully-trained DDPG Agent
Trial 0
