wassname/ray
1
0
Fork 0
mirror of https://github.com/wassname/ray.git synced 2026-07-01 18:39:41 +08:00
Code Issues Packages Projects Releases Wiki Activity

[RLlib] Increase the scope of RLlib's regression tests. (#12200)

Browse Source
This commit is contained in:
Sven Mika
2020-11-24 22:18:31 +01:00
committed by GitHub
parent 5895554555
commit 4afaa46028
5 changed files with 321 additions and 85 deletions
Download Patch File Download Diff File Expand all files Collapse all files
release/rllib_tests/regression_tests/compact-regression-tests-tf.yaml
+171
View File
@@ -0,0 +1,171 @@
# This file runs RLlib algorithm learning tests for select algorithms on TF.
# It is suggested to run these on a single g3.16xlarge or p3.16xl node
# in a DLAMI / tensorflow_p36 env.
# Note: RL runs are inherently high variance, so you'll have to check to
# see if the rewards reached seem reasonably in line with previous results.
# You can find the reference results here:
# https://github.com/ray-project/ray/tree/master/doc/dev/release_logs
a2c-tf-atari:
env: BreakoutNoFrameskip-v4
run: A2C
num_samples: 2
stop:
time_total_s: 3600
config:
framework: tf
rollout_fragment_length: 20
clip_rewards: True
num_workers: 5
num_envs_per_worker: 5
num_gpus: 1
lr_schedule: [
[0, 0.0007],
[20000000, 0.000000000001],
]
apex-dqn-tf-atari:
env: BreakoutNoFrameskip-v4
run: APEX
num_samples: 2
stop:
time_total_s: 3600
config:
framework: tf
double_q: false
dueling: false
num_atoms: 1
noisy: false
n_step: 3
lr: .0001
adam_epsilon: .00015
hiddens: [512]
buffer_size: 1000000
exploration_config:
epsilon_timesteps: 200000
final_epsilon: 0.01
prioritized_replay_alpha: 0.5
final_prioritized_replay_beta: 1.0
prioritized_replay_beta_annealing_timesteps: 2000000
num_gpus: 1
num_workers: 8
num_envs_per_worker: 8
rollout_fragment_length: 20
train_batch_size: 512
target_network_update_freq: 50000
timesteps_per_iteration: 25000
dqn-tf-atari:
env: BreakoutNoFrameskip-v4
run: DQN
num_samples: 2
stop:
time_total_s: 3600
config:
framework: tf
double_q: false
dueling: false
num_atoms: 1
noisy: false
prioritized_replay: false
n_step: 1
target_network_update_freq: 8000
lr: .0000625
adam_epsilon: .00015
hiddens: [512]
learning_starts: 20000
buffer_size: 1000000
rollout_fragment_length: 4
train_batch_size: 32
exploration_config:
epsilon_timesteps: 200000
final_epsilon: 0.01
prioritized_replay_alpha: 0.5
final_prioritized_replay_beta: 1.0
prioritized_replay_beta_annealing_timesteps: 2000000
num_gpus: 0.5
timesteps_per_iteration: 10000
impala-tf-atari:
env: BreakoutNoFrameskip-v4
run: IMPALA
num_samples: 2
stop:
time_total_s: 3600
config:
framework: tf
rollout_fragment_length: 50
train_batch_size: 500
num_workers: 10
num_envs_per_worker: 5
clip_rewards: True
lr_schedule: [
[0, 0.0005],
[20000000, 0.000000000001],
]
num_gpus: 1
ppo-tf-atari:
env: BreakoutNoFrameskip-v4
run: PPO
num_samples: 2
stop:
time_total_s: 3600
config:
framework: tf
lambda: 0.95
kl_coeff: 0.5
clip_rewards: True
clip_param: 0.1
vf_clip_param: 10.0
entropy_coeff: 0.01
train_batch_size: 5000
rollout_fragment_length: 100
sgd_minibatch_size: 500
num_sgd_iter: 10
num_workers: 10
num_envs_per_worker: 5
batch_mode: truncate_episodes
observation_filter: NoFilter
vf_share_layers: true
num_gpus: 1
# Expect roughly 1000 reward after 1h on 1GPU
sac-tf-halfcheetah-pybullet:
env: HalfCheetahBulletEnv-v0
run: SAC
num_samples: 2
stop:
time_total_s: 3600
config:
framework: tf
horizon: 1000
soft_horizon: false
Q_model:
fcnet_activation: relu
fcnet_hiddens: [256, 256]
policy_model:
fcnet_activation: relu
fcnet_hiddens: [256, 256]
tau: 0.005
target_entropy: auto
no_done_at_end: true
n_step: 1
rollout_fragment_length: 1
prioritized_replay: true
train_batch_size: 256
target_network_update_freq: 1
timesteps_per_iteration: 1000
learning_starts: 10000
optimization:
actor_learning_rate: 0.0003
critic_learning_rate: 0.0003
entropy_learning_rate: 0.0003
num_workers: 0
num_gpus: 1
clip_actions: false
normalize_actions: true
evaluation_interval: 1
metrics_smoothing_episodes: 5
release/rllib_tests/regression_tests/compact-regression-test.yaml → release/rllib_tests/regression_tests/compact-regression-tests-torch.yaml
+106 -82
View File
@@ -1,81 +1,37 @@
# This file runs on a single g3.16xl or p3.16xl node. It is suggested
# to run these in a DLAMI / tensorflow_p36 env. Note that RL runs are
# inherently high variance, so you'll have to check to see if the
# rewards reached seem reasonably in line with previous results.
#
# This file runs RLlib algorithm learning tests for select algorithms on torch.
# It is suggested to run these on a single g3.16xlarge or p3.16xl node
# in a DLAMI / tensorflow_p36 env.
# Note: RL runs are inherently high variance, so you'll have to check to
# see if the rewards reached seem reasonably in line with previous results.
# You can find the reference results here:
# https://github.com/ray-project/ray/tree/master/release/release_logs
atari-impala:
a2c-torch-atari:
env: BreakoutNoFrameskip-v4
run: IMPALA
num_samples: 4
stop:
time_total_s: 3600
config:
rollout_fragment_length: 50
train_batch_size: 500
num_workers: 10
num_envs_per_worker: 5
clip_rewards: True
lr_schedule: [
[0, 0.0005],
[20000000, 0.000000000001],
]
num_gpus: 1
atari-ppo-tf:
env: BreakoutNoFrameskip-v4
run: PPO
num_samples: 4
stop:
time_total_s: 3600
config:
lambda: 0.95
kl_coeff: 0.5
clip_rewards: True
clip_param: 0.1
vf_clip_param: 10.0
entropy_coeff: 0.01
train_batch_size: 5000
rollout_fragment_length: 100
sgd_minibatch_size: 500
num_sgd_iter: 10
num_workers: 10
num_envs_per_worker: 5
batch_mode: truncate_episodes
observation_filter: NoFilter
vf_share_layers: true
num_gpus: 1
atari-ppo-torch:
env: BreakoutNoFrameskip-v4
run: PPO
num_samples: 4
run: A2C
num_samples: 2
stop:
time_total_s: 3600
config:
framework: torch
lambda: 0.95
kl_coeff: 0.5
rollout_fragment_length: 20
clip_rewards: True
clip_param: 0.1
vf_clip_param: 10.0
entropy_coeff: 0.01
train_batch_size: 5000
rollout_fragment_length: 100
sgd_minibatch_size: 500
num_sgd_iter: 10
num_workers: 10
num_workers: 5
num_envs_per_worker: 5
batch_mode: truncate_episodes
observation_filter: NoFilter
vf_share_layers: true
num_gpus: 1
apex:
lr_schedule: [
[0, 0.0007],
[20000000, 0.000000000001],
]
apex-dqn-torch-atari:
env: BreakoutNoFrameskip-v4
run: APEX
num_samples: 4
num_samples: 2
stop:
time_total_s: 3600
config:
framework: torch
double_q: false
dueling: false
num_atoms: 1
@@ -98,29 +54,15 @@ apex:
train_batch_size: 512
target_network_update_freq: 50000
timesteps_per_iteration: 25000
atari-a2c:
env: BreakoutNoFrameskip-v4
run: A2C
num_samples: 4
stop:
time_total_s: 3600
config:
rollout_fragment_length: 20
clip_rewards: True
num_workers: 5
num_envs_per_worker: 5
num_gpus: 1
lr_schedule: [
[0, 0.0007],
[20000000, 0.000000000001],
]
atari-basic-dqn:
dqn-torch-atari:
env: BreakoutNoFrameskip-v4
run: DQN
num_samples: 4
num_samples: 2
stop:
time_total_s: 3600
config:
framework: torch
double_q: false
dueling: false
num_atoms: 1
@@ -141,5 +83,87 @@ atari-basic-dqn:
prioritized_replay_alpha: 0.5
final_prioritized_replay_beta: 1.0
prioritized_replay_beta_annealing_timesteps: 2000000
num_gpus: 0.2
num_gpus: 0.5
timesteps_per_iteration: 10000
impala-torch-atari:
env: BreakoutNoFrameskip-v4
run: IMPALA
num_samples: 2
stop:
time_total_s: 3600
config:
framework: torch
rollout_fragment_length: 50
train_batch_size: 500
num_workers: 10
num_envs_per_worker: 5
clip_rewards: True
lr_schedule: [
[0, 0.0005],
[20000000, 0.000000000001],
]
num_gpus: 1
ppo-torch-atari:
env: BreakoutNoFrameskip-v4
run: PPO
num_samples: 2
stop:
time_total_s: 3600
config:
framework: torch
lambda: 0.95
kl_coeff: 0.5
clip_rewards: True
clip_param: 0.1
vf_clip_param: 10.0
entropy_coeff: 0.01
train_batch_size: 5000
rollout_fragment_length: 100
sgd_minibatch_size: 500
num_sgd_iter: 10
num_workers: 10
num_envs_per_worker: 5
batch_mode: truncate_episodes
observation_filter: NoFilter
vf_share_layers: true
num_gpus: 1
# Expect roughly 1000 reward after 1h on 1GPU
sac-torch-halfcheetah-pybullet:
env: HalfCheetahBulletEnv-v0
run: SAC
num_samples: 2
stop:
time_total_s: 3600
config:
framework: torch
horizon: 1000
soft_horizon: false
Q_model:
fcnet_activation: relu
fcnet_hiddens: [256, 256]
policy_model:
fcnet_activation: relu
fcnet_hiddens: [256, 256]
tau: 0.005
target_entropy: auto
no_done_at_end: true
n_step: 1
rollout_fragment_length: 1
prioritized_replay: true
train_batch_size: 256
target_network_update_freq: 1
timesteps_per_iteration: 1000
learning_starts: 10000
optimization:
actor_learning_rate: 0.0003
critic_learning_rate: 0.0003
entropy_learning_rate: 0.0003
num_workers: 0
num_gpus: 1
clip_actions: false
normalize_actions: true
evaluation_interval: 1
metrics_smoothing_episodes: 5
release/rllib_tests/regression_tests/requirements.txt
+2 -1
View File
@@ -1 +1,2 @@
ray[rllib]
ray[rllib]
pybullet
release/rllib_tests/regression_tests/run.sh
+5 -2
View File
@@ -1,5 +1,5 @@
#!/usr/bin/env bash
ray_version=""
ray_version=""
commit=""
ray_branch=""
@@ -49,4 +49,7 @@ source activate tensorflow_p36 && pip install -U "$wheel"
source activate tensorflow_p36 && pip install "ray[rllib]" "ray[debug]"
source activate tensorflow_p36 && pip install torch==1.6 torchvision
source activate tensorflow_p36 && pip install boto3==1.4.8 cython==0.29.0
source activate tensorflow_p36 && rllib train -f compact-regression-test.yaml
# Run tf learning tests.
source activate tensorflow_p36 && rllib train -f compact-regression-tests-tf.yaml
# Run torch learning tests.
source activate tensorflow_p36 && rllib train -f compact-regression-tests-torch.yaml
rllib/tuned_examples/sac/halfcheetah-pybullet-sac.yaml
+37
View File
@@ -0,0 +1,37 @@
halfcheetah-pybullet-sac:
env: HalfCheetahBulletEnv-v0
run: SAC
stop:
episode_reward_mean: 9000
config:
# Works for both torch and tf.
framework: tf
horizon: 1000
soft_horizon: false
Q_model:
fcnet_activation: relu
fcnet_hiddens: [256, 256]
policy_model:
fcnet_activation: relu
fcnet_hiddens: [256, 256]
tau: 0.005
target_entropy: auto
no_done_at_end: true
n_step: 1
rollout_fragment_length: 1
prioritized_replay: true
train_batch_size: 256
target_network_update_freq: 1
timesteps_per_iteration: 1000
learning_starts: 10000
optimization:
actor_learning_rate: 0.0003
critic_learning_rate: 0.0003
entropy_learning_rate: 0.0003
num_workers: 0
num_gpus: 1
clip_actions: false
normalize_actions: true
evaluation_interval: 1
metrics_smoothing_episodes: 5