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[rllib] Fix APPO + continuous spaces, feed prev_rew/act to A3C properly #4286

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Merged
ericl merged 3 commits into from
Mar 7, 2019
Merged

[rllib] Fix APPO + continuous spaces, feed prev_rew/act to A3C properly #4286

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4521ef6
fix
ericl Mar 6, 2019
2351b19
fix a3c rew
ericl Mar 6, 2019
f5ff3ed
fix
ericl Mar 6, 2019
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87 changes: 47 additions & 40 deletions ci/jenkins_tests/run_rllib_tests.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -2,267 +2,274 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env PongDeterministic-v0 \
--run A3C \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
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@ericl ericl Mar 6, 2019

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Shorten to 1 to try to speed up jenkins.

--config '{"num_workers": 2}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env Pong-ram-v4 \
--run A3C \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env PongDeterministic-v0 \
--run A2C \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"free_log_std": true}}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"simple_optimizer": false, "num_sgd_iter": 2, "model": {"use_lstm": true}}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"simple_optimizer": true, "num_sgd_iter": 2, "model": {"use_lstm": true}}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_gpus": 0.1}' \
--ray-num-gpus 1

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "use_gae": false, "batch_mode": "complete_episodes"}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"remote_worker_envs": true, "num_envs_per_worker": 2, "num_workers": 1, "train_batch_size": 100, "sgd_minibatch_size": 50}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env Pendulum-v0 \
--run APPO \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2, "num_gpus": 0}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env Pendulum-v0 \
--run ES \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env Pong-v0 \
--run ES \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v0 \
--run A3C \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"lr": 1e-3, "schedule_max_timesteps": 100000, "exploration_fraction": 0.1, "exploration_final_eps": 0.02, "dueling": false, "hiddens": [], "model": {"fcnet_hiddens": [64], "fcnet_activation": "relu"}}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v0 \
--run APEX \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2, "timesteps_per_iteration": 1000, "num_gpus": 0, "min_iter_time_s": 1}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env FrozenLake-v0 \
--run DQN \
--stop '{"training_iteration": 2}'
--stop '{"training_iteration": 1}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env FrozenLake-v0 \
--run PPO \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_sgd_iter": 10, "sgd_minibatch_size": 64, "train_batch_size": 1000, "num_workers": 1}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env PongDeterministic-v4 \
--run DQN \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"lr": 1e-4, "schedule_max_timesteps": 2000000, "buffer_size": 10000, "exploration_fraction": 0.1, "exploration_final_eps": 0.01, "sample_batch_size": 4, "learning_starts": 10000, "target_network_update_freq": 1000, "gamma": 0.99, "prioritized_replay": true}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env MontezumaRevenge-v0 \
--run PPO \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"dim": 40, "conv_filters": [[16, [8, 8], 4], [32, [4, 4], 2], [512, [5, 5], 1]]}}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v1 \
--run A3C \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2, "model": {"use_lstm": true}}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"sample_batch_size": 500, "num_workers": 1}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"sample_batch_size": 500, "use_pytorch": true}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"sample_batch_size": 500, "num_workers": 1, "model": {"use_lstm": true, "max_seq_len": 100}}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"sample_batch_size": 500, "num_workers": 1, "num_envs_per_worker": 10}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env Pong-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"sample_batch_size": 500, "num_workers": 1}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env FrozenLake-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"sample_batch_size": 500, "num_workers": 1}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env Pendulum-v0 \
--run DDPG \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 1}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "model": {"use_lstm": true}}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0, "model": {"use_lstm": true}}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env MountainCarContinuous-v0 \
--run DDPG \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 1}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env MountainCarContinuous-v0 \
--run DDPG \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 1}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env Pendulum-v0 \
--run APEX_DDPG \
--ray-num-cpus 8 \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env Pendulum-v0 \
--run APEX_DDPG \
--ray-num-cpus 8 \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1, "batch_mode": "complete_episodes", "parameter_noise": true}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v0 \
--run MARWIL \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"input": "/ray/python/ray/rllib/tests/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "shuffle_buffer_size": 10}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"input": "/ray/python/ray/rllib/tests/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "soft_q": true}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
Expand Down Expand Up @@ -375,14 +382,14 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env PongDeterministic-v4 \
--run A3C \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2, "use_pytorch": true, "sample_async": false, "model": {"use_lstm": false, "grayscale": true, "zero_mean": false, "dim": 84}, "preprocessor_pref": "rllib"}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/tests/run_silent.sh train.py \
--env CartPole-v1 \
--run A3C \
--stop '{"training_iteration": 2}' \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2, "use_pytorch": true, "sample_async": false}'

docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
Expand Down
30 changes: 19 additions & 11 deletions python/ray/rllib/agents/a3c/a3c_tf_policy_graph.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -50,12 +50,13 @@ def __init__(self, observation_space, action_space, config):
tf.float32, [None] + list(observation_space.shape))
dist_class, logit_dim = ModelCatalog.get_action_dist(
action_space, self.config["model"])
prev_actions = ModelCatalog.get_action_placeholder(action_space)
prev_rewards = tf.placeholder(tf.float32, [None], name="prev_reward")
self.prev_actions = ModelCatalog.get_action_placeholder(action_space)
self.prev_rewards = tf.placeholder(
tf.float32, [None], name="prev_reward")
self.model = ModelCatalog.get_model({
"obs": self.observations,
"prev_actions": prev_actions,
"prev_rewards": prev_rewards,
"prev_actions": self.prev_actions,
"prev_rewards": self.prev_rewards,
"is_training": self._get_is_training_placeholder(),
}, observation_space, logit_dim, self.config["model"])
action_dist = dist_class(self.model.outputs)
Expand Down Expand Up @@ -83,8 +84,8 @@ def __init__(self, observation_space, action_space, config):
loss_in = [
("obs", self.observations),
("actions", actions),
("prev_actions", prev_actions),
("prev_rewards", prev_rewards),
("prev_actions", self.prev_actions),
("prev_rewards", self.prev_rewards),
("advantages", advantages),
("value_targets", self.v_target),
]
Expand All @@ -103,8 +104,8 @@ def __init__(self, observation_space, action_space, config):
loss_inputs=loss_in,
state_inputs=self.model.state_in,
state_outputs=self.model.state_out,
prev_action_input=prev_actions,
prev_reward_input=prev_rewards,
prev_action_input=self.prev_actions,
prev_reward_input=self.prev_rewards,
seq_lens=self.model.seq_lens,
max_seq_len=self.config["model"]["max_seq_len"])

Expand Down Expand Up @@ -138,7 +139,9 @@ def postprocess_trajectory(self,
next_state = []
for i in range(len(self.model.state_in)):
next_state.append([sample_batch["state_out_{}".format(i)][-1]])
last_r = self._value(sample_batch["new_obs"][-1], *next_state)
last_r = self._value(sample_batch["new_obs"][-1],
sample_batch["actions"][-1],
sample_batch["rewards"][-1], *next_state)
return compute_advantages(sample_batch, last_r, self.config["gamma"],
self.config["lambda"])

Expand All @@ -159,8 +162,13 @@ def extra_compute_action_fetches(self):
TFPolicyGraph.extra_compute_action_fetches(self),
**{"vf_preds": self.vf})

def _value(self, ob, *args):
feed_dict = {self.observations: [ob], self.model.seq_lens: [1]}
def _value(self, ob, prev_action, prev_reward, *args):
feed_dict = {
self.observations: [ob],
self.prev_actions: [prev_action],
self.prev_rewards: [prev_reward],
self.model.seq_lens: [1]
}
assert len(args) == len(self.model.state_in), \
(args, self.model.state_in)
for k, v in zip(self.model.state_in, args):
Expand Down
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