mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
446cbdf2e0
* Add `RandomEnv` example to examples folder. Convert warning into Error message when using an LSTM in a non-shared-vf network (after the warning, the program would crash). * LINT. * Fix issue #6884. LSTM + non-shared vf NN + PPO crashes when using a Tuple action space. * LINT * Change warning message for Model: shared_vf=False, LSTM=True cases. * Bug fix. * Add examples/random_env.py test to Jenkins.
492 lines
24 KiB
Bash
Executable file
492 lines
24 KiB
Bash
Executable file
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_catalog.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_optimizers.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_filters.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_evaluators.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_eager_support.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env PongDeterministic-v0 \
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--run A3C \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pong-ram-v4 \
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--run A3C \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env PongDeterministic-v0 \
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--run A2C \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v1 \
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--run PPO \
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--stop '{"training_iteration": 1}' \
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--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"free_log_std": true}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v1 \
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--run PPO \
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--stop '{"training_iteration": 1}' \
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--config '{"simple_optimizer": false, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v1 \
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--run PPO \
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--stop '{"training_iteration": 1}' \
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--config '{"simple_optimizer": true, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v1 \
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--run PPO \
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--stop '{"training_iteration": 1}' \
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--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "use_gae": false, "batch_mode": "complete_episodes"}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v1 \
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--run PPO \
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--stop '{"training_iteration": 1}' \
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--config '{"remote_worker_envs": true, "remote_env_batch_wait_ms": 99999999, "num_envs_per_worker": 2, "num_workers": 1, "train_batch_size": 100, "sgd_minibatch_size": 50}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v1 \
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--run PPO \
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--stop '{"training_iteration": 2}' \
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--config '{"remote_worker_envs": true, "num_envs_per_worker": 2, "num_workers": 1, "train_batch_size": 100, "sgd_minibatch_size": 50}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pendulum-v0 \
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--run APPO \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2, "num_gpus": 0}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pendulum-v0 \
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--run ES \
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--stop '{"training_iteration": 1}' \
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--config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pong-v0 \
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--run ES \
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--stop '{"training_iteration": 1}' \
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--config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run A3C \
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--stop '{"training_iteration": 1}' \
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run DQN \
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--stop '{"training_iteration": 1}' \
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--config '{"lr": 1e-3, "schedule_max_timesteps": 100000, "exploration_fraction": 0.1, "exploration_final_eps": 0.02, "dueling": false, "hiddens": [], "model": {"fcnet_hiddens": [64], "fcnet_activation": "relu"}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run DQN \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run APEX \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2, "timesteps_per_iteration": 1000, "num_gpus": 0, "min_iter_time_s": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env FrozenLake-v0 \
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--run DQN \
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--stop '{"training_iteration": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env FrozenLake-v0 \
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--run PPO \
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--stop '{"training_iteration": 1}' \
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--config '{"num_sgd_iter": 10, "sgd_minibatch_size": 64, "train_batch_size": 1000, "num_workers": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env PongDeterministic-v4 \
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--run DQN \
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--stop '{"training_iteration": 1}' \
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--config '{"lr": 1e-4, "schedule_max_timesteps": 2000000, "buffer_size": 10000, "exploration_fraction": 0.1, "exploration_final_eps": 0.01, "sample_batch_size": 4, "learning_starts": 10000, "target_network_update_freq": 1000, "gamma": 0.99, "prioritized_replay": true}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env MontezumaRevenge-v0 \
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--run PPO \
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--stop '{"training_iteration": 1}' \
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--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"dim": 40, "conv_filters": [[16, [8, 8], 4], [32, [4, 4], 2], [512, [5, 5], 1]]}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v1 \
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--run A3C \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2, "model": {"use_lstm": true}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run DQN \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run PG \
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--stop '{"training_iteration": 1}' \
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--config '{"sample_batch_size": 500, "num_workers": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run PG \
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--stop '{"training_iteration": 1}' \
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--config '{"sample_batch_size": 500, "use_pytorch": true}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run PG \
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--stop '{"training_iteration": 1}' \
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--config '{"sample_batch_size": 500, "num_workers": 1, "model": {"use_lstm": true, "max_seq_len": 100}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run PG \
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--stop '{"training_iteration": 1}' \
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--config '{"sample_batch_size": 500, "num_workers": 1, "num_envs_per_worker": 10}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pong-v0 \
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--run PG \
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--stop '{"training_iteration": 1}' \
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--config '{"sample_batch_size": 500, "num_workers": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env FrozenLake-v0 \
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--run PG \
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--stop '{"training_iteration": 1}' \
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--config '{"sample_batch_size": 500, "num_workers": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pendulum-v0 \
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--run DDPG \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run IMPALA \
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--stop '{"training_iteration": 1}' \
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--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run IMPALA \
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--stop '{"training_iteration": 1}' \
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--config '{"num_gpus": 0, "num_workers": 2, "num_aggregation_workers": 2, "min_iter_time_s": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run IMPALA \
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--stop '{"training_iteration": 1}' \
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--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "model": {"use_lstm": true}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run IMPALA \
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--stop '{"training_iteration": 1}' \
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--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run IMPALA \
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--stop '{"training_iteration": 1}' \
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--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0, "model": {"use_lstm": true}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env MountainCarContinuous-v0 \
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--run DDPG \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env MountainCarContinuous-v0 \
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--run DDPG \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pendulum-v0 \
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--run APEX_DDPG \
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--ray-num-cpus 8 \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pendulum-v0 \
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--run APEX_DDPG \
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--ray-num-cpus 8 \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1, "batch_mode": "complete_episodes", "parameter_noise": false}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run MARWIL \
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--stop '{"training_iteration": 1}' \
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--config '{"input": "/ray/rllib/tests/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "shuffle_buffer_size": 10}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--config '{"input": "/ray/rllib/tests/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "soft_q": true}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_local.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_dependency.py
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/ray/ci/suppress_output python /ray/rllib/tests/test_io.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_rollout_worker.py
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/ray/ci/suppress_output python /ray/rllib/tests/test_nested_spaces.py
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/ray/ci/suppress_output python /ray/rllib/tests/test_external_env.py
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/ray/ci/suppress_output python /ray/rllib/tests/test_external_multi_agent_env.py
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/ray/ci/suppress_output python /ray/rllib/examples/custom_keras_model.py --run=A2C --stop=50
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/examples/custom_keras_model.py --run=PPO --stop=50
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/examples/custom_keras_model.py --run=DQN --stop=50
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/examples/parametric_action_cartpole.py --run=PG --stop=50
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/examples/parametric_action_cartpole.py --run=PPO --stop=50
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/examples/parametric_action_cartpole.py --run=DQN --stop=50
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_lstm.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=PPO
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=PG
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=DQN
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=DDPG
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_multi_agent_env.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_supported_spaces.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/tests/test_rollout.sh
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# Run all single-agent regression tests (3x retry each)
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for yaml in $(ls $ROOT_DIR/../../rllib/tuned_examples/regression_tests); do
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/run_regression_tests.py \
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/ray/rllib/tuned_examples/regression_tests/$yaml
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done
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# Try a couple times since it's stochastic
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/multiagent_pendulum.py || \
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/multiagent_pendulum.py || \
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/ray/ci/suppress_output python /ray/rllib/tests/multiagent_pendulum.py
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/ray/ci/suppress_output python /ray/rllib/examples/multiagent_cartpole.py --num-iters=2
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/ray/ci/suppress_output python /ray/rllib/examples/multiagent_two_trainers.py --num-iters=2
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/ray/ci/suppress_output python /ray/rllib/tests/test_avail_actions_qmix.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/examples/cartpole_lstm.py --run=PPO --stop=200
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/ray/ci/suppress_output python /ray/rllib/examples/cartpole_lstm.py --run=IMPALA --stop=100
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/examples/cartpole_lstm.py --stop=200 --use-prev-action-reward
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/ray/ci/suppress_output python /ray/rllib/examples/custom_loss.py --iters=2
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/ray/ci/suppress_output python /ray/rllib/examples/rollout_worker_custom_workflow.py
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/ray/ci/suppress_output python /ray/rllib/examples/eager_execution.py --iters=2
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/ray/ci/suppress_output python /ray/rllib/examples/custom_tf_policy.py --iters=2
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/ray/ci/suppress_output python /ray/rllib/examples/custom_torch_policy.py --iters=2
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/ray/ci/suppress_output python /ray/rllib/examples/rollout_worker_custom_workflow.py
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/ray/ci/suppress_output python /ray/rllib/examples/custom_metrics_and_callbacks.py --num-iters=2
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/ray/ci/suppress_output python /ray/rllib/contrib/random_agent/random_agent.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/contrib/alpha_zero/examples/train_cartpole.py --training-iteration=1
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/ray/ci/suppress_output python /ray/rllib/examples/centralized_critic.py --stop=2000
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/ray/ci/suppress_output python /ray/rllib/examples/centralized_critic_2.py --stop=2000
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/ray/ci/suppress_output python /ray/rllib/examples/twostep_game.py --stop=2000 --run=contrib/MADDPG
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/ray/ci/suppress_output python /ray/rllib/examples/twostep_game.py --stop=2000 --run=PG
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/examples/twostep_game.py --stop=2000 --run=QMIX
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/examples/twostep_game.py --stop=2000 --run=APEX_QMIX
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/ray/ci/suppress_output python /ray/rllib/examples/autoregressive_action_dist.py --stop=150
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env PongDeterministic-v4 \
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--run A3C \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2, "use_pytorch": true, "sample_async": false, "model": {"use_lstm": false, "grayscale": true, "zero_mean": false, "dim": 84}, "preprocessor_pref": "rllib"}'
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--config '{"num_workers": 2, "use_pytorch": true, "sample_async": false}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pendulum-v0 \
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--run A3C \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2, "use_pytorch": true, "sample_async": false}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env PongDeterministic-v4 \
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--run IMPALA \
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--stop='{"timesteps_total": 40000}' \
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--ray-object-store-memory=1000000000 \
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--config '{"num_workers": 1, "num_gpus": 0, "num_envs_per_worker": 32, "sample_batch_size": 50, "train_batch_size": 50, "learner_queue_size": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/agents/impala/vtrace_test.py
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/ray/ci/suppress_output python /ray/rllib/tests/test_ignore_worker_failure.py
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/ray/ci/suppress_output python /ray/rllib/examples/custom_keras_rnn_model.py --run=PPO --stop=50 --env=RepeatAfterMeEnv
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/ray/ci/suppress_output python /ray/rllib/examples/custom_keras_rnn_model.py --run=PPO --stop=50 --env=RepeatInitialEnv
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/ray/ci/suppress_output python /ray/rllib/tests/test_env_with_subprocess.py
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/ray/ci/suppress_output python /ray/rllib/examples/random_env.py
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