def simulate_episodes(
config, params, graph, cleanups, expensive_summaries, gif_summary, name):
def env_ctor():
env = params.task.env_ctor()
if params.save_episode_dir:
env = control.wrappers.CollectGymDataset(env, params.save_episode_dir)
env = control.wrappers.ConcatObservation(env, ['image'])
return env
bind_or_none = lambda x, **kw: x and functools.partial(x, **kw)
cell = graph.cell
agent_config = tools.AttrDict(
cell=cell,
encoder=graph.encoder,
planner=functools.partial(params.planner, graph=graph),
objective=bind_or_none(params.objective, graph=graph),
exploration=params.exploration,
preprocess_fn=config.preprocess_fn,
postprocess_fn=config.postprocess_fn)
params = params.copy()
with params.unlocked:
params.update(agent_config)
with agent_config.unlocked:
agent_config.update(params)
summary, return_, cleanup = control.simulate(
graph.step, env_ctor, params.task.max_length,
params.num_agents, agent_config, config.isolate_envs,
expensive_summaries, gif_summary, name=name)
cleanups.append(cleanup) # Work around tf.cond() tensor return type.
return summary, return_
def simulate_episodes(config, params, graph, name):
def env_ctor():
env = params.task.env_ctor()
if params.save_episode_dir:
env = control.wrappers.CollectGymDataset(env, params.save_episode_dir)
env = control.wrappers.ConcatObservation(env, ['image'])
return env
cell = graph.cell
agent_config = tools.AttrDict(
cell=cell,
encoder=graph.encoder,
planner=params.planner,
objective=functools.partial(params.objective, graph=graph),
exploration=params.exploration,
preprocess_fn=config.preprocess_fn,
postprocess_fn=config.postprocess_fn)
params = params.copy()
params.update(agent_config)
agent_config.update(params)
# Batch size larger crashes so we simulate the episodes individually.
summaries, returns = [], []
for index in range(params.batch_size):
# with tf.control_dependencies(summaries + returns):
with tf.variable_scope('simulate-{}'.format(index + 1)):
summary, return_ = control.simulate(
graph.step, env_ctor, params.task.max_length,
1, agent_config, name=name)
summaries.append(summary)
returns.append(return_)
summary = tf.summary.merge(summaries)
return_ = tf.reduce_mean(returns)
return summary, return_
def simulate_episodes(config, params, graph, expensive_summaries, name):
def env_ctor():
env = params.task.env_ctor()
if params.save_episode_dir:
env = control.wrappers.CollectGymDataset(env,
params.save_episode_dir)
env = control.wrappers.ConcatObservation(env, ['image'])
return env
cell = graph.cell
agent_config = tools.AttrDict(cell=cell,
encoder=graph.encoder,
planner=params.planner,
objective=functools.partial(params.objective,
graph=graph),
exploration=params.exploration,
preprocess_fn=config.preprocess_fn,
postprocess_fn=config.postprocess_fn)
params = params.copy()
params.update(agent_config)
agent_config.update(params)
summary, return_ = control.simulate(graph.step,
env_ctor,
params.task.max_length,
params.num_agents,
agent_config,
config.isolate_envs,
expensive_summaries,
name=name)
return summary, return_
def simulate_episodes(config, params, graph, cleanups, expensive_summaries,
gif_summary, name):
def env_ctor():
#print("---------INSIDE ENV CTOR----------")
env = params.task.env_ctor()
#print("---CONFIG-----",config)
#print("------PARAMS ALL ------",params)
#print("--PARAMS TASK---",params.task)
#print("---NAME---", name)
#print("------ENV--",env)
#assert 1==2
if params.save_episode_dir:
env = control.wrappers.CollectGymDataset(
env,
params.save_episode_dir,
logdir=params.curious_dir,
is_curious=params.is_curious)
env = control.wrappers.ConcatObservation(env, ['image'])
return env
#print("---------------SIMULATE EPISODES------------------")
bind_or_none = lambda x, **kw: x and functools.partial(x, **kw)
cell = graph.cell
agent_config = tools.AttrDict(cell=cell,
encoder=graph.encoder,
planner=functools.partial(params.planner,
graph=graph),
objective=bind_or_none(params.objective,
graph=graph),
exploration=params.exploration,
preprocess_fn=config.preprocess_fn,
postprocess_fn=config.postprocess_fn,
num_models=config.num_models)
params = params.copy()
with params.unlocked:
params.update(agent_config)
with agent_config.unlocked:
agent_config.update(params)
summary, return_, cleanup = control.simulate(graph.step,
env_ctor,
params.task.max_length,
params.num_agents,
agent_config,
config.isolate_envs,
expensive_summaries,
gif_summary,
name=name)
cleanups.append(cleanup) # Work around tf.cond() tensor return type.
return summary, return_
