def test(config, env):
ob_space = env.observation_space
ac_space = env.action_space
tf.reset_default_graph()
gpu_opts = tf.GPUOptions(allow_growth=True)
tf_config = tf.ConfigProto(
inter_op_parallelism_threads=1,
intra_op_parallelism_threads=1,
gpu_options=gpu_opts,
)
with tf.Session(config=tf_config) as sess:
config.batch_size = 2
config.number_of_steps = 2
policy = build_policy(env, 'cnn')
model = Model(policy=policy,
env=env,
nsteps=config.number_of_steps,
ent_coef=config.entropy_weight,
vf_coef=config.critic_weight,
max_grad_norm=config.max_grad_norm,
lr=config.learning_rate,
alpha=config.rmsp_decay,
epsilon=config.discount_factor,
total_timesteps=config.timesteps,
lrschedule='linear')
model.load(config.load_path)
return make_rollouts(config, env, model)
def test(config, env):
ob_space = env.observation_space
ac_space = env.action_space
tf.reset_default_graph()
gpu_opts = tf.GPUOptions(allow_growth=True)
tf_config = tf.ConfigProto(
inter_op_parallelism_threads=1,
intra_op_parallelism_threads=1,
gpu_options=gpu_opts,
)
with tf.Session(config=tf_config) as sess:
policy = build_policy(env, 'cnn', estimate_q=True)
model = Model(policy=policy,
ob_space=ob_space,
ac_space=ac_space,
nenvs=config.number_of_environments,
nsteps=config.number_of_steps,
ent_coef=config.entropy_weight,
q_coef=config.critic_weight,
gamma=config.discount_factor,
max_grad_norm=config.max_grad_norm,
lr=config.learning_rate,
rprop_alpha=config.rmsp_decay,
rprop_epsilon=config.rmsp_epsilon,
total_timesteps=config.timesteps,
lrschedule='linear',
c=config.clipping_factor,
trust_region=True,
alpha=config.momentum,
delta=config.trust_region_delta)
tf_util.load_variables(config.load_path, sess=sess)
return make_rollouts(config, env, model)
def test(config, env):
ob_space = env.observation_space
ac_space = env.action_space
tf.reset_default_graph()
gpu_opts = tf.GPUOptions(allow_growth=True)
tf_config = tf.ConfigProto(
inter_op_parallelism_threads=1,
intra_op_parallelism_threads=1,
gpu_options=gpu_opts,
)
with tf.Session(config=tf_config) as sess:
nenvs = env.num_envs
nbatch = nenvs * config.number_of_steps
nbatch_train = nbatch // 4
policy = build_policy(env, 'cnn')
model = Model(
policy=policy,
ob_space=ob_space,
ac_space=ac_space,
nbatch_act=nenvs,
nbatch_train=nbatch_train,
nsteps=config.number_of_steps,
ent_coef=config.entropy_weight,
vf_coef=config.critic_weight,
max_grad_norm=config.max_grad_norm,
comm=None,
mpi_rank_weight=1
)
model.load(config.load_path)
return make_rollouts(config, env, model)
def test(config, env):
tf.reset_default_graph()
gpu_opts = tf.GPUOptions(allow_growth=True)
tf_config = tf.ConfigProto(
inter_op_parallelism_threads=1,
intra_op_parallelism_threads=1,
gpu_options=gpu_opts,
)
with tf.Session(config=tf_config) as session:
model = Model(config, env, session)
tf_util.load_variables(config.load_path, sess=session)
return make_rollouts(config, env, model)