def __init__(self, max_angle=12, max_num_steps=1000):
self.env = CartPoleEnv()
# self.env.theta_threshold_radians = max_angle * 2 * math.pi / 360
self.observation_space = self.env.observation_space
self.action_space = self.env.action_space
self.step_counter = 0
self.max_num_steps = max_num_steps
def __init__(self, noise_type='uniform', noise_scale=0.0, init_scale=0.0):
self.noise_type = noise_type
assert self.noise_type in ['normal', 'uniform']
self.noise_scale = noise_scale
self.init_scale = init_scale
CartPoleEnv.__init__(self)
def run_cartpole_reinforce(args, log_dir="./logs/reinforce"):
os.makedirs(log_dir, exist_ok=True)
env = CartPoleEnv()
agent: PolicyAgent = PolicyAgent(env.observation_space.shape[0],
env.action_space.n)
env.seed(args.seed)
torch.manual_seed(args.seed)
env = BenchMonitor(env, log_dir, allow_early_resets=True)
train(env, agent, args)
return agent, env
def test_random_agent():
from agentos.agents import RandomAgent
from gym.envs.classic_control import CartPoleEnv
environment = CartPoleEnv()
environment.reset()
agent = RandomAgent(environment=environment)
done = agent.advance()
assert not done, "CartPole never finishes after one random step."
run_agent(agent)
def run_cartpole_dqn(num_batches=1000,
batch_size=32,
log_dir="./logs/dqn",
seed=0):
os.makedirs(log_dir, exist_ok=True)
env = CartPoleEnv()
env.seed(seed)
torch.manual_seed(seed)
agent = CartPoleAgent(env.observation_space, env.action_space)
from baselines.bench import Monitor as BenchMonitor
env = BenchMonitor(env, log_dir, allow_early_resets=True)
train(agent, env, num_batches=num_batches, batch_size=batch_size)
return agent, env
def get():
env = CartPoleEnv()
for i_episode in range(10000):
observation = env.reset()
action = chose_action(model=model)
while True:
observation_, reward, done, info = env.step(action)
x, x_dot, theta, theta_dot = observation_
r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8
r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5
reward = r1 + r2
ransition = np.hstack((observation, [action, reward], observation_))
print()
def save():
env = CartPoleEnv()
total_steps = 0
memory = []
memory_counter = 0
for i_episode in range(100):
observation = env.reset()
while True:
env.render()
action = env.action_space.sample()
observation_, reward, done, info = env.step(action)
x, x_dot, theta, theta_dot = observation_
r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8
r2 = (env.theta_threshold_radians -
abs(theta)) / env.theta_threshold_radians - 0.5
reward = r1 + r2
transition = np.hstack((observation, [action,
reward], observation_))
memory.append(transition)
if done:
break
observation = observation_
total_steps += 1
memory = np.array(memory)
np.save("memory.npy", memory)
env.close()
def go2():
env = CartPoleEnv()
episode_step_counter = 0
for i_episode in range(10000):
action = env.reset()
step_counter = 0
while True:
env.render()
# 随机选择一个action
# 获取环境给予的奖励
observation_, reward, done, info = env.step(action)
x, x_dot, theta, theta_dot = observation_
r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8
r2 = (env.theta_threshold_radians -
abs(theta)) / env.theta_threshold_radians - 0.5
reward = r1 + r2
print(reward)
step_counter = step_counter + 1
if done:
episode_step_counter += step_counter
# print("第{}回合，坚持了{}步".format(i_episode, step_counter))
print("平均步数:{}".format(episode_step_counter / (i_episode + 1)))
break
env.close()
def run_cartpole_a2c(args: A2CParams, log_dir="./logs/a2c"):
os.makedirs(log_dir, exist_ok=True)
env = CartPoleEnv()
env = BenchMonitor(env, log_dir, allow_early_resets=True)
env = CartPoleEnvSelfReset(env)
# env.seed(params.seed)
# torch.manual_seed(params.seed)
agent: CartPoleA2CAgent = CartPoleA2CAgent(env.observation_space,
env.action_space, args)
exp_mem = build_experience_memory(agent, env, args.num_rollout_steps)
w = World(env, agent, exp_mem)
with torch.no_grad():
w.agent.eval()
gather_exp_via_rollout(w.env, w.agent, w.exp_mem,
args.num_rollout_steps)
optimizer = torch.optim.Adam(agent.parameters(), args.lr)
for k in tqdm(range(args.num_batches)):
with torch.no_grad():
w.agent.eval()
rollout = collect_experiences_calc_advantage(w, args)
train_batch(w.agent, rollout, optimizer)
return agent, env
class TFPolicy(agentos.Policy):
def __init__(self, tf_model):
self.tf_model = tf_model
self.observation_space = CartPoleEnv().observation_space
self.action_space = CartPoleEnv().action_space
def compute_action(self, obs):
assert self.observation_space.contains(obs), obs
action = self.tf_model(np.array(obs)[np.newaxis])
env_compatible_action = int(round(action.numpy()[0][0]))
assert self.action_space.contains(
env_compatible_action), env_compatible_action
return env_compatible_action
def __deepcopy__(self, memo):
return TFPolicy(keras.models.clone_model(self.tf_model))
def test_order_enforcing():
"""Checks that the order enforcing works as expected, raising an error before reset is called and not after."""
# The reason for not using gym.make is that all environments are by default wrapped in the order enforcing wrapper
env = CartPoleEnv()
assert not has_wrapper(env, OrderEnforcing)
# Assert that the order enforcing works for step and render before reset
order_enforced_env = OrderEnforcing(env)
assert order_enforced_env._has_reset is False
with pytest.raises(ResetNeeded):
order_enforced_env.step(0)
with pytest.raises(ResetNeeded):
order_enforced_env.render(mode="rgb_array")
assert order_enforced_env._has_reset is False
# Assert that the Assertion errors are not raised after reset
order_enforced_env.reset()
assert order_enforced_env._has_reset is True
order_enforced_env.step(0)
order_enforced_env.render(mode="rgb_array")
# Assert that with disable_render_order_enforcing works, the environment has already been reset
env = CartPoleEnv()
env = OrderEnforcing(env, disable_render_order_enforcing=True)
env.render(mode="rgb_array") # no assertion error
def go():
env = CartPoleEnv()
total_steps = 0
memory = []
model = create_model()
epsilon = 0.9
memory_counter = 1000
for i_episode in range(1000):
observation = env.reset()
ep_r = 0
while True:
env.render()
if np.random.uniform() < epsilon:
actions_value = model.predict(np.array([observation]))
action = np.argmax(actions_value)
else:
action = env.action_space.sample()
observation_, reward, done, info = env.step(action)
x, x_dot, theta, theta_dot = observation_
r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8
r2 = (env.theta_threshold_radians -
abs(theta)) / env.theta_threshold_radians - 0.5
reward = r1 + r2
transition = np.hstack((observation, [action,
reward], observation_))
memory.append(transition)
if len(memory) > memory_counter:
xx, yy = get_data(np.array(memory), model)
print(xx.shape)
model.fit(xx, yy, epochs=10)
epsilon = epsilon + 0.00001
memory = []
# memory_counter = memory_counter + 5
ep_r = ep_r + reward
if done:
# print(ep_r)
break
observation = observation_
total_steps += 1
model.save("logs/cp.h5")
model.summary()
env.close()
def test_openai_gym(self):
self.start_tests(name='openai-gym')
self.unittest(environment=dict(environment='gym', level='CartPole-v1'),
num_episodes=2)
from gym.envs.classic_control import CartPoleEnv
self.unittest(environment=dict(environment='gym',
level=CartPoleEnv(),
max_episode_timesteps=100),
num_episodes=2)
def run_cartpole_reinforce(args, log_dir="./logs/reinforce"):
os.makedirs(log_dir, exist_ok=True)
env = CartPoleEnv()
agent = CartPoleReinforceAgent(env.observation_space.shape[0], env.action_space.n)
env.seed(args.seed)
torch.manual_seed(args.seed)
env = BenchMonitor(env, log_dir, allow_early_resets=True)
env = CartPoleEnvSelfReset(env)
exp_mem = build_experience_memory(agent, env, args.num_rollout_steps)
w = World(env, agent, exp_mem)
with torch.no_grad():
w.agent.eval()
gather_exp_via_rollout(w.env, w.agent, w.exp_mem, args.num_rollout_steps)
optimizer = torch.optim.Adam(agent.parameters(), args.lr)
for k in tqdm(range(args.num_batches)):
with torch.no_grad():
agent.eval()
batch = do_rollout(w, args)
train_batch(agent, batch, optimizer)
return agent, env
def test_discrete_vectorized_original_equality(self):
venv = DiscreteVectorizedCartPoleEnv()
state, action = self.state_action
action = (action > 0).astype(int)
dim1, dim2 = self.dims
venv.state = state
vobs, vreward, vdone, _ = venv.step(action)
env = CartPoleEnv()
for i in range(dim1):
for j in range(dim2):
env.reset()
env.state = state[i, j]
obs, reward, done, _ = env.step(int(action[i, j, 0]))
np.testing.assert_allclose(obs, vobs[i, j])
np.testing.assert_allclose(reward, vreward[i, j])
np.testing.assert_allclose(done, vdone[i, j])
class CartPoleDictEnvWrapper(gym.Env):
def __init__(self, max_angle=12, max_num_steps=1000):
self.env = CartPoleEnv()
# self.env.theta_threshold_radians = max_angle * 2 * math.pi / 360
self.observation_space = self.env.observation_space
self.action_space = self.env.action_space
self.step_counter = 0
self.max_num_steps = max_num_steps
def step(self, action):
if isinstance(action, numpy.ndarray):
action = action[0]
assert isinstance(action, numpy.int64)
obs, _, done, _ = self.env.step(action)
self.step_counter += 1
if self.step_counter % self.max_num_steps == 0:
done = True
if done:
reward = -10.0
obs = self.env.reset()
else:
reward = 0.0
return {"observation": obs, "reward": reward, "done": int(done)}
def reset(self):
obs = self.env.reset()
return {"observation": obs, "reward": 0.0, "done": int(False)}
def render(self, mode="human"):
return self.env.render(mode)
def close(self):
self.env.close()
def seed(self, seed=None):
return self.env.seed(seed)
from tensorforce.environments.openai_gym import OpenAIGym
from env_gym import SimplePendulumEnv
from gym.envs.classic_control import CartPoleEnv
from tensorforce.execution import Runner
import os
batch_size = 10
n_step = 2000
# Instantiate the environment
n_env = 12
list_envs = []
# env = OpenAIGym(SimplePendulumEnv())
env = OpenAIGym(CartPoleEnv())
actor_network = [
dict(type='dense', size=128, activation='relu'),
dict(type='dense', size=64, activation='relu'),
dict(type='dense', size=64, activation='relu')
]
critic_network = [
dict(type='dense', size=128, activation='relu'),
dict(type='dense', size=64, activation='relu'),
dict(type='dense', size=64, activation='relu')
]
agent = Agent.create(agent='ppo',
batch_size=batch_size,
import gym from gym.envs.classic_control import CartPoleEnv env = CartPoleEnv() env = env.unwrapped # 不做这个会有很多限制 print(env.action_space) # 查看这个环境中可用的 action 有多少个 print(env.observation_space) # 查看这个环境中可用的 state 的 observation 有多少个 print(env.observation_space.high) # 查看 observation 最高取值 print(env.observation_space.low)
def __init__(self, tf_model):
self.tf_model = tf_model
self.observation_space = CartPoleEnv().observation_space
self.action_space = CartPoleEnv().action_space
return int(
max(0, round(self.nn(np.array(obs)[np.newaxis]).numpy()[0][0])))
class RandomTFAgent(agentos.Agent):
def __init__(self, environment, policy):
super().__init__(environment=environment, policy=policy)
self.ret_vals = []
def advance(self):
trajs = agentos.rollout(self.policy, self.environment, max_steps=2000)
self.ret_vals.append(sum(trajs.rewards))
if __name__ == "__main__":
from gym.envs.classic_control import CartPoleEnv
random_nn_agent = RandomTFAgent(
environment=CartPoleEnv,
policy=SingleLayerTFPolicy(
CartPoleEnv().action_space,
CartPoleEnv().observation_space,
),
)
agentos.run_agent(random_nn_agent, max_iters=10)
print(f"Agent done!\n"
f"Num rollouts: {len(random_nn_agent.ret_vals)}\n"
f"Avg return: {np.mean(random_nn_agent.ret_vals)}\n"
f"Max return: {max(random_nn_agent.ret_vals)}\n"
f"Median return: {np.median(random_nn_agent.ret_vals)}\n")
for i_episode in range(10000):
observation = env.reset()
action = chose_action(model=model)
while True:
observation_, reward, done, info = env.step(action)
x, x_dot, theta, theta_dot = observation_
r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8
r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5
reward = r1 + r2
ransition = np.hstack((observation, [action, reward], observation_))
print()
if __name__ == '__main__':
env = CartPoleEnv()
for i_episode in range(20):
observation = env.reset()
for t in range(100):
env.render()
action = env.action_space.sample()
observation_, reward, done, info = env.step(action)
x, x_dot, theta, theta_dot = observation_
r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8
r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5
reward = r1 + r2
print(reward)
ransition = np.hstack((observation, [action, reward], observation_))
print(ransition)
import numpy as np
import scipy as sp
from gym.envs.classic_control import CartPoleEnv
from util.policy_nn_boltzmann import *
from util.learner_nn import *
from util.util_cartpole import *
import sys
np.set_printoptions(precision=6)
np.set_printoptions(suppress=True)
mdp = CartPoleEnv()
mdp.horizon = 200
agent_policy = nnBoltzmannPolicy(nStateFeatures=4,
nActions=2,
nHiddenNeurons=64,
paramInitMaxVal=0.025)
agent_learner = nnGpomdpLearner(mdp, agent_policy, gamma=0.995)
#eps = collect_pendulum_episodes(mdp,agent_policy,10,mdp.horizon)
#agent_policy.optimize_gradient(eps,0.003)
ctlearn(agent_learner,
steps=10000,
nEpisodes=25,
learningRate=0.003,
plotGradient=True,
printInfo=True)
import os
from collections import deque
import matplotlib.pyplot as plt
import numpy as np
from gym.envs.classic_control import CartPoleEnv
from tensorboardX import SummaryWriter
from training.dqn.dqn_agent import Agent
from utility.Scheduler import Scheduler
currentDT = datetime.datetime.now()
print(f'Start at {currentDT.strftime("%Y-%m-%d %H:%M:%S")}')
seed = 5
# np.random.seed(seed)
env = CartPoleEnv() # gym.make("CartPole-v0")
env.seed(seed)
np.random.seed(seed)
state_size = 4
action_size = 2
STARTING_BETA = 0.6 # the higher the more it decreases the influence of high TD transitions
ALPHA = 0.6 # the higher the more aggressive the sampling towards high TD transitions
EPS_DECAY = 0.2
MIN_EPS = 0.01
current_time = currentDT.strftime('%b%d_%H-%M-%S')
comment = f"alpha={ALPHA}, min_eps={MIN_EPS}, eps_decay={EPS_DECAY}"
log_dir = os.path.join('../runs', current_time + '_' + comment)
os.mkdir(log_dir)
print(f"logging to {log_dir}")
writer = SummaryWriter(log_dir=log_dir)
def run_task(snapshot_config, *_):
"""Set up environment and algorithm and run the task.
Args:
snapshot_config (garage.experiment.SnapshotConfig): The snapshot
configuration used by LocalRunner to create the snapshotter.
If None, it will create one with default settings.
_ : Unused parameters
"""
th = 1.8
g_max = 0.1
#delta = 1e-7
if args.env == 'CartPole':
#CartPole
env = TfEnv(normalize(CartPoleEnv()))
runner = LocalRunner(snapshot_config)
batch_size = 5000
max_length = 100
n_timestep = 5e5
n_counts = 5
name = 'CartPole'
grad_factor = 5
th = 1.2
#batchsize: 1
# lr = 0.1
# w = 2
# c = 50
#batchsize: 50
lr = 0.75
c = 3
w = 2
discount = 0.995
path = './init/CartPole_policy.pth'
if args.env == 'Walker':
#Walker_2d
env = TfEnv(normalize(Walker2dEnv()))
runner = LocalRunner(snapshot_config)
batch_size = 50000
max_length = 500
n_timestep = 1e7
n_counts = 5
lr = 0.75
w = 2
c = 12
grad_factor = 6
discount = 0.999
name = 'Walk'
path = './init/Walk_policy.pth'
if args.env == 'HalfCheetah':
env = TfEnv(normalize(HalfCheetahEnv()))
runner = LocalRunner(snapshot_config)
batch_size = 50000
max_length = 500
n_timestep = 1e7
n_counts = 5
lr = 0.6
w = 1
c = 4
grad_factor = 5
th = 1.2
g_max = 0.06
discount = 0.999
name = 'HalfCheetah'
path = './init/HalfCheetah_policy.pth'
if args.env == 'Hopper':
#Hopper
env = TfEnv(normalize(HopperEnv()))
runner = LocalRunner(snapshot_config)
batch_size = 50000
max_length = 1000
th = 1.5
n_timestep = 1e7
n_counts = 5
lr = 0.75
w = 1
c = 3
grad_factor = 6
g_max = 0.15
discount = 0.999
name = 'Hopper'
path = './init/Hopper_policy.pth'
for i in range(n_counts):
# print(env.spec)
if args.env == 'CartPole':
policy = CategoricalMLPPolicy(env.spec,
hidden_sizes=[8, 8],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
else:
policy = GaussianMLPPolicy(env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
policy.load_state_dict(torch.load(path))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = MBPG_HA(env_spec=env.spec,
env = env,
env_name= name,
policy=policy,
baseline=baseline,
max_path_length=max_length,
discount=discount,
grad_factor=grad_factor,
policy_lr= lr,
c = c,
w = w,
th=th,
g_max=g_max,
n_timestep=n_timestep,
batch_size=batch_size,
center_adv=True,
# delta=delta
#decay_learning_rate=d_lr,
)
runner.setup(algo, env)
runner.train(n_epochs=100, batch_size=batch_size)
parser = argparse.ArgumentParser(
description="Run reinforce with a simple TF policy on gym CartPole. "
"One rollout per call to agent.advance(), "
"200 steps per rollout.", )
parser.add_argument(
"max_iters",
type=int,
metavar="MAX_ITERS",
help="How many times to call advance() on agent.",
)
parser.add_argument("--rollouts_per_iter", type=int, default=1)
parser.add_argument("--max_steps_per_rollout", type=int, default=200)
parser.add_argument("--discount_rate", type=float, default=0.9)
args = parser.parse_args()
reinforce_agent = ReinforceAgent(
CartPoleEnv(),
TwoLayerTFPolicy(),
rollouts_per_iter=args.rollouts_per_iter,
max_steps_per_rollout=args.max_steps_per_rollout,
discount_rate=args.discount_rate,
)
agentos.run_agent(
reinforce_agent,
max_iters=args.max_iters,
)
print("Agent done!")
if reinforce_agent.ret_vals:
print(f"Num rollouts: {len(reinforce_agent.ret_vals)}\n"
f"Avg return: {np.mean(reinforce_agent.ret_vals)}\n"
f"Max return: {max(reinforce_agent.ret_vals)}\n"
f"Median return: {np.median(reinforce_agent.ret_vals)}\n")