def simulate(step,
env_ctor,
duration,
num_agents,
agent_config,
isolate_envs='none',
expensive_summaries=False,
name='simulate'):
summaries = []
with tf.variable_scope(name):
return_, image, action, reward = collect_rollouts(
step=step,
env_ctor=env_ctor,
duration=duration,
num_agents=num_agents,
agent_config=agent_config,
isolate_envs=isolate_envs)
return_mean = tf.reduce_mean(return_)
summaries.append(tf.summary.scalar('return', return_mean))
if expensive_summaries:
summaries.append(tf.summary.histogram('return_hist', return_))
summaries.append(tf.summary.histogram('reward_hist', reward))
summaries.append(tf.summary.histogram('action_hist', action))
summaries.append(
tools.image_strip_summary('image', image, max_length=duration))
summaries.append(
tools.gif_summary('animation', image, max_outputs=1, fps=20))
summary = tf.summary.merge(summaries)
return summary, return_mean
def simulate(step,
env_ctor,
duration,
num_agents,
agent_config,
env_processes=False,
name='simulate'):
summaries = []
with tf.variable_scope(name):
return_, image, action, reward = collect_rollouts(
step=step,
env_ctor=env_ctor,
duration=duration, # i.e. max_length
num_agents=num_agents,
agent_config=agent_config,
env_processes=env_processes)
return_mean = tf.reduce_mean(return_)
summaries.append(tf.summary.histogram('return_hist', return_))
summaries.append(tf.summary.scalar('return', return_mean))
summaries.append(tf.summary.histogram('reward_hist', reward))
summaries.append(tf.summary.histogram('action_hist', action))
summaries.append(
tools.image_strip_summary('image', image, max_length=duration))
summary = tf.summary.merge(summaries)
return summary, return_mean
