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[tests] Make end-to-end tests more stable #3697

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Merged
merged 8 commits into from
Mar 27, 2020
Merged

[tests] Make end-to-end tests more stable #3697

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dccbfab
Tweak simple RL test
Mar 25, 2020
fcf5bf2
Try to fix SAC simple RL
Mar 25, 2020
1877528
Make tests look for max reward
Mar 26, 2020
4274785
Increase time penalty
Mar 26, 2020
2b30c9a
Change default thresh to 0.9, fix SAC recurrent
Mar 26, 2020
8ba3aa5
Extend recurrent SAC
Mar 26, 2020
a7fcb63
Lower learning rate for SAC Recurrent
Mar 26, 2020
f5bbdaa
Lower ghost test threshold
Mar 26, 2020
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2 changes: 1 addition & 1 deletion ml-agents/mlagents/trainers/tests/simple_test_envs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
VIS_OBS_SIZE = (20, 20, 3)
STEP_SIZE = 0.1

TIME_PENALTY = 0.001
TIME_PENALTY = 0.01
MIN_STEPS = int(1.0 / STEP_SIZE) + 1
SUCCESS_REWARD = 1.0 + MIN_STEPS * TIME_PENALTY

Expand Down
24 changes: 16 additions & 8 deletions ml-agents/mlagents/trainers/tests/test_simple_rl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@
lambd: 0.95
learning_rate: 5.0e-3
learning_rate_schedule: constant
max_steps: 2000
max_steps: 3000
memory_size: 16
normalize: false
num_epoch: 3
Expand Down Expand Up @@ -96,6 +96,9 @@ def generate_config(
# Custom reward processors shuld be built within the test function and passed to _check_environment_trains
# Default is average over the last 5 final rewards
def default_reward_processor(rewards, last_n_rewards=5):
rewards_to_use = rewards[-last_n_rewards:]
# For debugging tests
print("Last {} rewards:".format(last_n_rewards), rewards_to_use)
return np.array(rewards[-last_n_rewards:], dtype=np.float32).mean()


Expand Down Expand Up @@ -124,7 +127,7 @@ def _check_environment_trains(
trainer_config,
reward_processor=default_reward_processor,
meta_curriculum=None,
success_threshold=0.99,
success_threshold=0.9,
env_manager=None,
):
# Create controller and begin training.
Expand Down Expand Up @@ -168,7 +171,6 @@ def _check_environment_trains(
if (
success_threshold is not None
): # For tests where we are just checking setup and not reward

processed_rewards = [
reward_processor(rewards) for rewards in env.final_rewards.values()
]
Expand Down Expand Up @@ -253,11 +255,11 @@ def test_simple_sac(use_discrete):
@pytest.mark.parametrize("use_discrete", [True, False])
def test_2d_sac(use_discrete):
env = SimpleEnvironment(
[BRAIN_NAME], use_discrete=use_discrete, action_size=2, step_size=0.5
[BRAIN_NAME], use_discrete=use_discrete, action_size=2, step_size=0.8
)
override_vals = {"buffer_init_steps": 2000, "max_steps": 3000}
override_vals = {"buffer_init_steps": 2000, "max_steps": 4000}
config = generate_config(SAC_CONFIG, override_vals)
_check_environment_trains(env, config)
_check_environment_trains(env, config, success_threshold=0.8)


@pytest.mark.parametrize("use_discrete", [True, False])
Expand Down Expand Up @@ -301,7 +303,13 @@ def test_visual_advanced_sac(vis_encode_type, num_visual):
@pytest.mark.parametrize("use_discrete", [True, False])
def test_recurrent_sac(use_discrete):
env = MemoryEnvironment([BRAIN_NAME], use_discrete=use_discrete)
override_vals = {"batch_size": 32, "use_recurrent": True, "max_steps": 2000}
override_vals = {
"batch_size": 64,
"use_recurrent": True,
"max_steps": 3000,
"learning_rate": 1e-3,
"buffer_init_steps": 500,
}
config = generate_config(SAC_CONFIG, override_vals)
_check_environment_trains(env, config)

Expand Down Expand Up @@ -343,7 +351,7 @@ def test_simple_ghost_fails(use_discrete):
processed_rewards = [
default_reward_processor(rewards) for rewards in env.final_rewards.values()
]
success_threshold = 0.99
success_threshold = 0.9
assert any(reward > success_threshold for reward in processed_rewards) and any(
reward < success_threshold for reward in processed_rewards
)
Expand Down