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rollouts.py
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rollouts.py
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from collections import deque, defaultdict
import numpy as np
from mpi4py import MPI
from recorder import Recorder
class Rollout(object):
def __init__(self, ob_space, ac_space, nenvs, nsteps_per_seg, nsegs_per_env, nlumps, envs, policy,
int_rew_coeff, ext_rew_coeff, record_rollouts, dynamics):
self.nenvs = nenvs
self.nsteps_per_seg = nsteps_per_seg
self.nsegs_per_env = nsegs_per_env
self.nsteps = self.nsteps_per_seg * self.nsegs_per_env
self.ob_space = ob_space
self.ac_space = ac_space
self.nlumps = nlumps
self.lump_stride = nenvs // self.nlumps
self.envs = envs
self.policy = policy
self.dynamics = dynamics
self.reward_fun = lambda ext_rew, int_rew: ext_rew_coeff * np.clip(ext_rew, -1., 1.) + int_rew_coeff * int_rew
self.buf_vpreds = np.empty((nenvs, self.nsteps), np.float32)
self.buf_nlps = np.empty((nenvs, self.nsteps), np.float32)
self.buf_rews = np.empty((nenvs, self.nsteps), np.float32)
self.buf_ext_rews = np.empty((nenvs, self.nsteps), np.float32)
self.buf_acs = np.empty((nenvs, self.nsteps, *self.ac_space.shape), self.ac_space.dtype)
self.buf_obs = np.empty((nenvs, self.nsteps, *self.ob_space.shape), self.ob_space.dtype)
self.buf_obs_last = np.empty((nenvs, self.nsegs_per_env, *self.ob_space.shape), np.float32)
self.buf_news = np.zeros((nenvs, self.nsteps), np.float32)
self.buf_new_last = self.buf_news[:, 0, ...].copy()
self.buf_vpred_last = self.buf_vpreds[:, 0, ...].copy()
self.env_results = [None] * self.nlumps
# self.prev_feat = [None for _ in range(self.nlumps)]
# self.prev_acs = [None for _ in range(self.nlumps)]
self.int_rew = np.zeros((nenvs,), np.float32)
self.recorder = Recorder(nenvs=self.nenvs, nlumps=self.nlumps) if record_rollouts else None
self.statlists = defaultdict(lambda: deque([], maxlen=100))
self.stats = defaultdict(float)
self.best_ext_ret = None
self.all_visited_rooms = []
self.all_scores = []
self.step_count = 0
def collect_rollout(self):
self.ep_infos_new = []
for t in range(self.nsteps):
self.rollout_step()
self.calculate_reward()
self.update_info()
def calculate_reward(self):
int_rew = self.dynamics.calculate_loss(ob=self.buf_obs,
last_ob=self.buf_obs_last,
acs=self.buf_acs)
self.buf_rews[:] = self.reward_fun(int_rew=int_rew, ext_rew=self.buf_ext_rews)
def rollout_step(self):
t = self.step_count % self.nsteps
s = t % self.nsteps_per_seg
for l in range(self.nlumps):
obs, prevrews, news, infos = self.env_get(l)
# if t > 0:
# prev_feat = self.prev_feat[l]
# prev_acs = self.prev_acs[l]
for info in infos:
epinfo = info.get('episode', {})
mzepinfo = info.get('mz_episode', {})
retroepinfo = info.get('retro_episode', {})
epinfo.update(mzepinfo)
epinfo.update(retroepinfo)
if epinfo:
if "n_states_visited" in info:
epinfo["n_states_visited"] = info["n_states_visited"]
epinfo["states_visited"] = info["states_visited"]
self.ep_infos_new.append((self.step_count, epinfo))
sli = slice(l * self.lump_stride, (l + 1) * self.lump_stride)
acs, vpreds, nlps = self.policy.get_ac_value_nlp(obs)
self.env_step(l, acs)
# self.prev_feat[l] = dyn_feat
# self.prev_acs[l] = acs
self.buf_obs[sli, t] = obs
self.buf_news[sli, t] = news
self.buf_vpreds[sli, t] = vpreds
self.buf_nlps[sli, t] = nlps
self.buf_acs[sli, t] = acs
if t > 0:
self.buf_ext_rews[sli, t - 1] = prevrews
# if t > 0:
# dyn_logp = self.policy.call_reward(prev_feat, pol_feat, prev_acs)
#
# int_rew = dyn_logp.reshape(-1, )
#
# self.int_rew[sli] = int_rew
# self.buf_rews[sli, t - 1] = self.reward_fun(ext_rew=prevrews, int_rew=int_rew)
if self.recorder is not None:
self.recorder.record(timestep=self.step_count, lump=l, acs=acs, infos=infos, int_rew=self.int_rew[sli],
ext_rew=prevrews, news=news)
self.step_count += 1
if s == self.nsteps_per_seg - 1:
for l in range(self.nlumps):
sli = slice(l * self.lump_stride, (l + 1) * self.lump_stride)
nextobs, ext_rews, nextnews, _ = self.env_get(l)
self.buf_obs_last[sli, t // self.nsteps_per_seg] = nextobs
if t == self.nsteps - 1:
self.buf_new_last[sli] = nextnews
self.buf_ext_rews[sli, t] = ext_rews
_, self.buf_vpred_last[sli], _ = self.policy.get_ac_value_nlp(nextobs)
# dyn_logp = self.policy.call_reward(self.prev_feat[l], last_pol_feat, prev_acs)
# dyn_logp = dyn_logp.reshape(-1, )
# int_rew = dyn_logp
#
# self.int_rew[sli] = int_rew
# self.buf_rews[sli, t] = self.reward_fun(ext_rew=ext_rews, int_rew=int_rew)
def update_info(self):
all_ep_infos = MPI.COMM_WORLD.allgather(self.ep_infos_new)
all_ep_infos = sorted(sum(all_ep_infos, []), key=lambda x: x[0])
if all_ep_infos:
all_ep_infos = [i_[1] for i_ in all_ep_infos] # remove the step_count
keys_ = all_ep_infos[0].keys()
all_ep_infos = {k: [i[k] for i in all_ep_infos] for k in keys_}
self.statlists['eprew'].extend(all_ep_infos['r'])
self.stats['eprew_recent'] = np.mean(all_ep_infos['r'])
self.statlists['eplen'].extend(all_ep_infos['l'])
self.stats['epcount'] += len(all_ep_infos['l'])
self.stats['tcount'] += sum(all_ep_infos['l'])
if 'visited_rooms' in keys_:
# Montezuma specific logging.
self.stats['visited_rooms'] = sorted(list(set.union(*all_ep_infos['visited_rooms'])))
self.stats['pos_count'] = np.mean(all_ep_infos['pos_count'])
self.all_visited_rooms.extend(self.stats['visited_rooms'])
self.all_scores.extend(all_ep_infos["r"])
self.all_scores = sorted(list(set(self.all_scores)))
self.all_visited_rooms = sorted(list(set(self.all_visited_rooms)))
if MPI.COMM_WORLD.Get_rank() == 0:
print("All visited rooms")
print(self.all_visited_rooms)
print("All scores")
print(self.all_scores)
if 'levels' in keys_:
# Retro logging
temp = sorted(list(set.union(*all_ep_infos['levels'])))
self.all_visited_rooms.extend(temp)
self.all_visited_rooms = sorted(list(set(self.all_visited_rooms)))
if MPI.COMM_WORLD.Get_rank() == 0:
print("All visited levels")
print(self.all_visited_rooms)
current_max = np.max(all_ep_infos['r'])
else:
current_max = None
self.ep_infos_new = []
if current_max is not None:
if (self.best_ext_ret is None) or (current_max > self.best_ext_ret):
self.best_ext_ret = current_max
self.current_max = current_max
def env_step(self, l, acs):
self.envs[l].step_async(acs)
self.env_results[l] = None
def env_get(self, l):
if self.step_count == 0:
ob = self.envs[l].reset()
out = self.env_results[l] = (ob, None, np.ones(self.lump_stride, bool), {})
else:
if self.env_results[l] is None:
out = self.env_results[l] = self.envs[l].step_wait()
else:
out = self.env_results[l]
return out