class SpaceSession(Session):
'''Session for multi-agent/env setting'''
def __init__(self, spec, info_space, global_nets=None):
self.spec = spec
self.info_space = info_space
self.index = self.info_space.get('session')
util.set_session_logger(self.spec, self.info_space, logger)
self.data = None
self.aeb_space = AEBSpace(self.spec, self.info_space)
self.env_space = EnvSpace(self.spec, self.aeb_space)
self.aeb_space.init_body_space()
util.set_rand_seed(self.info_space.get_random_seed(), self.env_space)
util.try_set_cuda_id(self.spec, self.info_space)
assert not ps.is_dict(
global_nets
), f'multi agent global_nets must be a list of dicts, got {global_nets}'
self.agent_space = AgentSpace(self.spec, self.aeb_space, global_nets)
logger.info(util.self_desc(self))
logger.info(f'Initialized session {self.index}')
def save_if_ckpt(self, agent_space, env_space):
'''Save for agent, env if episode is at checkpoint'''
for agent in agent_space.agents:
for body in agent.nanflat_body_a:
env = body.env
super(SpaceSession, self).save_if_ckpt(agent, env)
def run_all_episodes(self):
'''
Continually run all episodes, where each env can step and reset at its own clock_speed and timeline.
Will terminate when all envs done are done.
'''
all_done = self.aeb_space.tick('epi')
reward_space, state_space, done_space = self.env_space.reset()
self.agent_space.reset(state_space)
while not all_done:
all_done = self.aeb_space.tick()
action_space = self.agent_space.act(state_space)
reward_space, state_space, done_space = self.env_space.step(
action_space)
self.agent_space.update(action_space, reward_space, state_space,
done_space)
self.save_if_ckpt(self.agent_space, self.env_space)
def close(self):
'''
Close session and clean up.
Save agent, close env.
'''
self.agent_space.close()
self.env_space.close()
logger.info('Session done and closed.')
def run(self):
self.run_all_episodes()
self.data = analysis.analyze_session(self) # session fitness
self.close()
return self.data
class SpaceSession(Session):
'''Session for multi-agent/env setting'''
def __init__(self, spec, info_space, global_nets=None):
self.spec = spec
self.info_space = info_space
self.index = self.info_space.get('session')
util.set_logger(self.spec, self.info_space, logger, 'session')
self.data = None
self.eval_proc = None # reference run_online_eval process
self.aeb_space = AEBSpace(self.spec, self.info_space)
self.env_space = EnvSpace(self.spec, self.aeb_space)
self.aeb_space.init_body_space()
util.set_rand_seed(self.info_space.get_random_seed(), self.env_space)
util.try_set_cuda_id(self.spec, self.info_space)
self.agent_space = AgentSpace(self.spec, self.aeb_space, global_nets)
logger.info(util.self_desc(self))
logger.info(f'Initialized session {self.index}')
def try_ckpt(self, agent_space, env_space):
'''Try to checkpoint agent and run_online_eval at the start, save_freq, and the end'''
for agent in agent_space.agents:
for body in agent.nanflat_body_a:
env = body.env
super(SpaceSession, self).try_ckpt(agent, env)
def run_all_episodes(self):
'''
Continually run all episodes, where each env can step and reset at its own clock_speed and timeline.
Will terminate when all envs done are done.
'''
all_done = self.aeb_space.tick('epi')
reward_space, state_space, done_space = self.env_space.reset()
self.agent_space.reset(state_space)
while not all_done:
self.try_ckpt(self.agent_space, self.env_space)
all_done = self.aeb_space.tick()
action_space = self.agent_space.act(state_space)
reward_space, state_space, done_space = self.env_space.step(
action_space)
self.agent_space.update(action_space, reward_space, state_space,
done_space)
self.try_ckpt(self.agent_space, self.env_space)
if self.eval_proc is not None: # wait for final eval before closing
util.run_cmd_wait(self.eval_proc)
def close(self):
'''
Close session and clean up.
Save agent, close env.
'''
self.agent_space.close()
self.env_space.close()
logger.info('Session done and closed.')
def run(self):
self.run_all_episodes()
self.data = analysis.analyze_session(self) # session fitness
self.close()
return self.data