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reinforce.py
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reinforce.py
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import argparse
import logging
import gym
import numpy as np
try:
import tensorflow as tf
import tensorflow.compat.v1 as tf1
except ImportError:
raise ImportError("reinforcement requires tensorflow 1.14")
from example.baselines import ValueBaseline
from example.log_utils import NoLog
from example.policies import ParameterizedPolicy
from reinforcement.algorithm.reinforce import Reinforce
from reinforcement.agents.basis import BatchAgent
logger = logging.getLogger(__name__)
class Reporter:
def __init__(self, cfg):
self.cfg = cfg
def should_render(self, e):
return _matches_frequency(e, self.cfg.render_frq)
def should_log(self, e):
return _matches_frequency(e, self.cfg.log_frq)
def report(self, e, rs):
r, lf = rs[-1], self.cfg.log_frq
return f"Episode {e}: reward={r}; mean reward of last {lf} episodes: {np.mean(rs[-lf:])}"
def _matches_frequency(e, f):
return f > 0 and e % f == 0
def run_reinforce(config):
reporter, env, rewards = Reporter(config), gym.make('CartPole-v0'), []
with tf1.Session() as session:
agent = _make_agent(config, session, env)
for episode in range(1, config.episodes + 1):
reward = _run_episode(env, episode, agent, reporter)
rewards.append(reward)
if reporter.should_log(episode):
logger.info(reporter.report(episode, rewards))
env.close()
def _make_agent(config, session, env):
p = ParameterizedPolicy(session, env.observation_space.shape[0], env.action_space.n, NoLog(), config.lr_policy)
b = ValueBaseline(session, env.observation_space.shape[0], NoLog(), config.lr_baseline)
alg = Reinforce(p, config.gamma, b, config.num_trajectories)
return BatchAgent(alg)
def _run_episode(env, episode, agent, report):
obs = env.reset()
done, reward = False, 0
while not done:
if report.should_render(episode):
env.render()
obs, r, done, _ = env.step(agent.next_action(obs))
agent.signal(r)
reward += r
agent.train()
return reward
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Run reinforce example.')
parser.add_argument('-e', '--episodes', type=int, default=3000, help='number of episodes to be run')
parser.add_argument('-n', '--num-trajectories', type=int, default=10,
help='number of trajectories used in training of agent')
parser.add_argument('-g', '--gamma', type=float, default=0.99, help='gamma used for reward accumulation')
parser.add_argument('--lr-policy', type=float, default=50, help='learning rate of policy ANN')
parser.add_argument('--lr-baseline', type=float, default=0.01, help='learning rate of baseline ANN')
parser.add_argument('--render-frq', type=int, default=0, help='render every x episode')
parser.add_argument('--log-frq', type=int, default=100, help='log every x episode')
parser.add_argument('--log-lvl', type=str, default="info", help='log level (default: info)')
args = parser.parse_args()
logging.basicConfig(level=getattr(logging, args.log_lvl.upper()))
run_reinforce(args)