def test():
# Parallel environments
n_cpu = 4
env = SubprocVecEnv([lambda: RSEnv() for i in range(n_cpu)])
model = A2C(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=600000, log_interval=10)
model.save("sba2c")
env = TestRSEnv()
obs = env.reset()
done = False
while not done:
action, _ = model.predict(obs)
obs, rewards, done, info = env.step(action)
env.render()
env.close()
from env.RSEnv import RSEnv from env.TestRSEnv import TestRSEnv from acme import environment_loop from acme import specs from acme import wrappers from acme.agents.tf import d4pg from acme.tf import networks from acme.tf import utils as tf2_utils from acme.utils import loggers import numpy as np import sonnet as snt import gym environment = RSEnv() environment = wrappers.GymWrapper(environment) # To dm_env interface. # Make sure the environment outputs single-precision floats. environment = wrappers.SinglePrecisionWrapper(environment) # Grab the spec of the environment. environment_spec = specs.make_environment_spec(environment) #@title Build agent networks # BUILDING A D4PG AGENT # Get total number of action dimensions from action spec. num_dimensions = np.prod(environment_spec.actions.shape, dtype=int) # Create the shared observation network; here simply a state-less operation.
import gym
from stable_baselines.common.policies import MlpPolicy
from stable_baselines import PPO1
from env.RSEnv import RSEnv
from env.TestRSEnv import TestRSEnv
env = RSEnv()
#model = PPO1(MlpPolicy, env, verbose=1)
model = PPO1.load("sbppov3")
model.set_env(env)
model.learn(total_timesteps=3000000,
log_interval=10,
reset_num_timesteps=False)
model.save("sbppov4")
env = TestRSEnv()
obs = env.reset()
done = False
while not done:
action, _ = model.predict(obs)
obs, rewards, done, info = env.step(action)
env.render()
env.close()
def _init():
env = RSEnv()
env.seed(seed + rank)
return env