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test_main.py
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test_main.py
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import numpy as np
from threading import Lock
import concurrent
import functools
import argparse
from copy import deepcopy
import gym
from cheetah_gym.envs.normalized_env import NormalizedEnv
from cheetah_gym.agents.evaluator import Evaluator
from cheetah_gym.agents.ddpg import DDPG
from cheetah_gym.agents.util import *
def train_single(agent):
env = gym.make('random1-v0')
nb_states = env.observation_space.shape[0]
nb_actions = env.action_space.shape[0]
agent_cpy = deepcopy(agent)
agent.is_training = True
step = episode = episode_steps = 0
episode_reward = 0.
observation = None
done = False
while not done:
# reset if it is the start of episode
if observation is None:
observation = deepcopy(env.reset())
agent.reset(observation)
# agent pick action ...
if step <= 100: #Warmup
action = agent.random_action()
else:
action = agent.select_action(observation)
# env response with next_observation, reward, terminate_info
observation2, reward, done, info = env.step(action)
observation2 = deepcopy(observation2)
if max_episode_length and episode_steps >= max_episode_length -1:
done = True
# agent observe and update policy
agent.observe(reward, observation2, done)
if step > 100 : #Warmup
agent.update_policy()
# update
step += 1
episode_steps += 1
episode_reward += reward
observation = deepcopy(observation2)
return [
observation,
agent.select_action(observation),
0.,
False]
def train2(num_iterations, agent, env, evaluate, validate_steps, output, max_episode_length=None, debug=False):
agent.is_training = True
episodes = episode_steps = 0
last_step = 0
observation = None
train_fn = functools.partial(train_single, agent)
NUM_WORKERS = 5
while episodes < num_iterations:
with concurrent.futures.ProcessPoolExecutor(max_workers=NUM_WORKERS) as executor:
futures = []
for _ in range(NUM_WORKERS):
futures.append(executor.submit(train_fn))
for future in futures:
agent.memory.append(future.result())
episodes+=1
def train(num_iterations, agent, env, evaluate, validate_steps, output, max_episode_length=None, debug=False):
agent.is_training = True
step = episode = episode_steps = 0
episode_reward = 0.
last_step = 0
observation = None
while step < num_iterations:
# reset if it is the start of episode
if observation is None:
observation = deepcopy(env.reset())
agent.reset(observation)
# agent pick action ...
if step <= 100: #Warmup
action = agent.random_action()
else:
action = agent.select_action(observation)
# env response with next_observation, reward, terminate_info
observation2, reward, done, info = env.step(action)
observation2 = deepcopy(observation2)
if max_episode_length and episode_steps >= max_episode_length -1:
done = True
# agent observe and update policy
agent.observe(reward, observation2, done)
if step > 100 : #Warmup
agent.update_policy()
# # [optional] evaluate
if evaluate is not None and validate_steps > 0 and step % validate_steps == 0:
policy = lambda x: agent.select_action(x, decay_epsilon=False)
validate_reward = evaluate(env, policy, debug=False, visualize=False)
if debug: prYellow(f'[Evaluate] Step_{step:07d}: mean_reward:{validate_reward}')
# [optional] save intermideate model
if episode % int(5) == 0:
agent.save_model(output)
# update
step += 1
episode_steps += 1
episode_reward += reward
observation = deepcopy(observation2)
if done: # end of episode
if debug: prGreen('#{}: episode_reward:{} steps:{}'.format(episode,episode_reward,step - last_step))
agent.memory.append(
observation,
agent.select_action(observation),
0., False
)
# reset
observation = None
episode_steps = 0
episode_reward = 0.
episode += 1
last_step = step
if __name__ == "__main__":
env = NormalizedEnv(gym.make('random1-v0'))
seed = 42
validate_steps = 2000
max_episode_length = 500
validate_episodes = 20
train_iter = 200000
if seed > 0:
np.random.seed(seed)
env.seed(seed)
nb_states = env.observation_space.shape[0]
nb_actions = env.action_space.shape[0]
agent = DDPG(nb_states, nb_actions, seed)
evaluate = Evaluator(validate_episodes,
validate_steps, "./cheetah_gym/agents/weights", max_episode_length=max_episode_length)
# train2(train_iter, agent, env, evaluate,
# validate_steps, "./cheetah_gym/agents/weights", max_episode_length=max_episode_length, debug=True)
train(train_iter, agent, env, evaluate,
validate_steps, "./cheetah_gym/agents/weights", max_episode_length=max_episode_length, debug=True)