diff --git a/ci/jenkins_tests/run_multi_node_tests.sh b/ci/jenkins_tests/run_multi_node_tests.sh
index 62db30625..0eb284abd 100755
--- a/ci/jenkins_tests/run_multi_node_tests.sh
+++ b/ci/jenkins_tests/run_multi_node_tests.sh
@@ -14,376 +14,12 @@ ROOT_DIR=$(cd "$(dirname "${BASH_SOURCE:-$0}")"; pwd)
 DOCKER_SHA=$($ROOT_DIR/../../build-docker.sh --output-sha --no-cache)
 echo "Using Docker image" $DOCKER_SHA
 
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env PongDeterministic-v0 \
-    --run A3C \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_workers": 2}'
+######################## RLLIB TESTS #################################
 
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env Pong-ram-v4 \
-    --run A3C \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_workers": 2}'
+source $ROOT_DIR/run_rllib_tests.sh
 
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env PongDeterministic-v0 \
-    --run A2C \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_workers": 2}'
 
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v1 \
-    --run PPO \
-    --stop '{"training_iteration": 2}' \
-    --config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"free_log_std": true}}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v1 \
-    --run PPO \
-    --stop '{"training_iteration": 2}' \
-    --config '{"simple_optimizer": false, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v1 \
-    --run PPO \
-    --stop '{"training_iteration": 2}' \
-    --config '{"simple_optimizer": true, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v1 \
-    --run PPO \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_gpus": 0.1}' \
-    --ray-num-gpus 1
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v1 \
-    --run PPO \
-    --stop '{"training_iteration": 2}' \
-    --config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "use_gae": false, "batch_mode": "complete_episodes"}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v1 \
-    --run PPO \
-    --stop '{"training_iteration": 2}' \
-    --config '{"remote_worker_envs": true, "num_envs_per_worker": 2, "num_workers": 1, "train_batch_size": 100, "sgd_minibatch_size": 50}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env Pendulum-v0 \
-    --run ES \
-    --stop '{"training_iteration": 2}' \
-    --config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env Pong-v0 \
-    --run ES \
-    --stop '{"training_iteration": 2}' \
-    --config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v0 \
-    --run A3C \
-    --stop '{"training_iteration": 2}' \
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v0 \
-    --run DQN \
-    --stop '{"training_iteration": 2}' \
-    --config '{"lr": 1e-3, "schedule_max_timesteps": 100000, "exploration_fraction": 0.1, "exploration_final_eps": 0.02, "dueling": false, "hiddens": [], "model": {"fcnet_hiddens": [64], "fcnet_activation": "relu"}}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v0 \
-    --run DQN \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_workers": 2}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v0 \
-    --run APEX \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_workers": 2, "timesteps_per_iteration": 1000, "num_gpus": 0, "min_iter_time_s": 1}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env FrozenLake-v0 \
-    --run DQN \
-    --stop '{"training_iteration": 2}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env FrozenLake-v0 \
-    --run PPO \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_sgd_iter": 10, "sgd_minibatch_size": 64, "train_batch_size": 1000, "num_workers": 1}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env PongDeterministic-v4 \
-    --run DQN \
-    --stop '{"training_iteration": 2}' \
-    --config '{"lr": 1e-4, "schedule_max_timesteps": 2000000, "buffer_size": 10000, "exploration_fraction": 0.1, "exploration_final_eps": 0.01, "sample_batch_size": 4, "learning_starts": 10000, "target_network_update_freq": 1000, "gamma": 0.99, "prioritized_replay": true}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env MontezumaRevenge-v0 \
-    --run PPO \
-    --stop '{"training_iteration": 2}' \
-    --config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"dim": 40, "conv_filters": [[16, [8, 8], 4], [32, [4, 4], 2], [512, [5, 5], 1]]}}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v1 \
-    --run A3C \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_workers": 2, "model": {"use_lstm": true}}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v0 \
-    --run DQN \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_workers": 2}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v0 \
-    --run PG \
-    --stop '{"training_iteration": 2}' \
-    --config '{"sample_batch_size": 500, "num_workers": 1}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v0 \
-    --run PG \
-    --stop '{"training_iteration": 2}' \
-    --config '{"sample_batch_size": 500, "use_pytorch": true}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v0 \
-    --run PG \
-    --stop '{"training_iteration": 2}' \
-    --config '{"sample_batch_size": 500, "num_workers": 1, "model": {"use_lstm": true, "max_seq_len": 100}}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v0 \
-    --run PG \
-    --stop '{"training_iteration": 2}' \
-    --config '{"sample_batch_size": 500, "num_workers": 1, "num_envs_per_worker": 10}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env Pong-v0 \
-    --run PG \
-    --stop '{"training_iteration": 2}' \
-    --config '{"sample_batch_size": 500, "num_workers": 1}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env FrozenLake-v0 \
-    --run PG \
-    --stop '{"training_iteration": 2}' \
-    --config '{"sample_batch_size": 500, "num_workers": 1}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env Pendulum-v0 \
-    --run DDPG \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_workers": 1}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v0 \
-    --run IMPALA \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v0 \
-    --run IMPALA \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "model": {"use_lstm": true}}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v0 \
-    --run IMPALA \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v0 \
-    --run IMPALA \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0, "model": {"use_lstm": true}}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env MountainCarContinuous-v0 \
-    --run DDPG \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_workers": 1}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    rllib train \
-    --env MountainCarContinuous-v0 \
-    --run DDPG \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_workers": 1}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env Pendulum-v0 \
-    --run APEX_DDPG \
-    --ray-num-cpus 8 \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env Pendulum-v0 \
-    --run APEX_DDPG \
-    --ray-num-cpus 8 \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1, "batch_mode": "complete_episodes", "parameter_noise": true}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v0 \
-    --run MARWIL \
-    --stop '{"training_iteration": 2}' \
-    --config '{"input": "/ray/python/ray/rllib/test/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "shuffle_buffer_size": 10}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v0 \
-    --run DQN \
-    --stop '{"training_iteration": 2}' \
-    --config '{"input": "/ray/python/ray/rllib/test/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "soft_q": true}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/test/test_local.py
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/test/test_io.py
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/test/test_checkpoint_restore.py
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/test/test_policy_evaluator.py
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/test/test_nested_spaces.py
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/test/test_external_env.py
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/parametric_action_cartpole.py --run=PG --stop=50
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/parametric_action_cartpole.py --run=PPO --stop=50
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/parametric_action_cartpole.py --run=DQN --stop=50
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/custom_loss.py --iters=2
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/test/test_lstm.py
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=PPO
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=PG
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=DQN
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=DDPG
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/test/test_multi_agent_env.py
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/test/test_supported_spaces.py
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/test/test_env_with_subprocess.py
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    /ray/python/ray/rllib/test/test_rollout.sh
-
-# Run all single-agent regression tests (3x retry each)
-for yaml in $(ls $ROOT_DIR/../../python/ray/rllib/tuned_examples/regression_tests); do
-    docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-        python /ray/python/ray/rllib/test/run_regression_tests.py /ray/python/ray/rllib/tuned_examples/regression_tests/$yaml
-done
-
-# Try a couple times since it's stochastic
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-        python /ray/python/ray/rllib/test/multiagent_pendulum.py || \
-    docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-        python /ray/python/ray/rllib/test/multiagent_pendulum.py || \
-    docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-        python /ray/python/ray/rllib/test/multiagent_pendulum.py
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/multiagent_cartpole.py --num-iters=2
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/multiagent_two_trainers.py --num-iters=2
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/test/test_avail_actions_qmix.py
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/cartpole_lstm.py --run=PPO --stop=200
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/cartpole_lstm.py --run=IMPALA --stop=100
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/cartpole_lstm.py --stop=200 --use-prev-action-reward
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/custom_metrics_and_callbacks.py --num-iters=2
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/contrib/random_agent/random_agent.py
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/twostep_game.py --stop=2000 --run=PG
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/twostep_game.py --stop=2000 --run=QMIX
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/examples/twostep_game.py --stop=2000 --run=APEX_QMIX
+######################## SGD TESTS #################################
 
 docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/experimental/sgd/test_sgd.py --num-iters=2 \
@@ -409,27 +45,7 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/experimental/sgd/mnist_example.py --num-iters=1 \
         --num-workers=1 --devices-per-worker=1 --strategy=ps --tune
 
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env PongDeterministic-v4 \
-    --run A3C \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_workers": 2, "use_pytorch": true, "sample_async": false, "model": {"use_lstm": false, "grayscale": true, "zero_mean": false, "dim": 84}, "preprocessor_pref": "rllib"}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env CartPole-v1 \
-    --run A3C \
-    --stop '{"training_iteration": 2}' \
-    --config '{"num_workers": 2, "use_pytorch": true, "sample_async": false}'
-
-docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
-    python /ray/python/ray/rllib/train.py \
-    --env PongDeterministic-v4 \
-    --run IMPALA \
-    --stop='{"timesteps_total": 40000}' \
-    --ray-object-store-memory=500000000 \
-    --config '{"num_workers": 1, "num_gpus": 0, "num_envs_per_worker": 64, "sample_batch_size": 50, "train_batch_size": 50, "learner_queue_size": 1}'
+######################## RAY BACKEND TESTS #################################
 
 python3 $ROOT_DIR/multi_node_docker_test.py \
     --docker-image=$DOCKER_SHA \
@@ -461,6 +77,7 @@ python3 $ROOT_DIR/multi_node_docker_test.py \
     --shm-size=60G \
     --test-script=/ray/ci/jenkins_tests/multi_node_tests/large_memory_test.py
 
+######################## TUNE TESTS #################################
 
 docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     pytest /ray/python/ray/tune/test/cluster_tests.py
@@ -528,5 +145,3 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
 docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     python /ray/python/ray/tune/examples/skopt_example.py \
     --smoke-test
-
-
diff --git a/ci/jenkins_tests/run_rllib_tests.sh b/ci/jenkins_tests/run_rllib_tests.sh
new file mode 100644
index 000000000..470f6bf07
--- /dev/null
+++ b/ci/jenkins_tests/run_rllib_tests.sh
@@ -0,0 +1,394 @@
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env PongDeterministic-v0 \
+    --run A3C \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_workers": 2}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env Pong-ram-v4 \
+    --run A3C \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_workers": 2}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env PongDeterministic-v0 \
+    --run A2C \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_workers": 2}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v1 \
+    --run PPO \
+    --stop '{"training_iteration": 2}' \
+    --config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"free_log_std": true}}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v1 \
+    --run PPO \
+    --stop '{"training_iteration": 2}' \
+    --config '{"simple_optimizer": false, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v1 \
+    --run PPO \
+    --stop '{"training_iteration": 2}' \
+    --config '{"simple_optimizer": true, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v1 \
+    --run PPO \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_gpus": 0.1}' \
+    --ray-num-gpus 1
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v1 \
+    --run PPO \
+    --stop '{"training_iteration": 2}' \
+    --config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "use_gae": false, "batch_mode": "complete_episodes"}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v1 \
+    --run PPO \
+    --stop '{"training_iteration": 2}' \
+    --config '{"remote_worker_envs": true, "num_envs_per_worker": 2, "num_workers": 1, "train_batch_size": 100, "sgd_minibatch_size": 50}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env Pendulum-v0 \
+    --run ES \
+    --stop '{"training_iteration": 2}' \
+    --config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env Pong-v0 \
+    --run ES \
+    --stop '{"training_iteration": 2}' \
+    --config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v0 \
+    --run A3C \
+    --stop '{"training_iteration": 2}' \
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v0 \
+    --run DQN \
+    --stop '{"training_iteration": 2}' \
+    --config '{"lr": 1e-3, "schedule_max_timesteps": 100000, "exploration_fraction": 0.1, "exploration_final_eps": 0.02, "dueling": false, "hiddens": [], "model": {"fcnet_hiddens": [64], "fcnet_activation": "relu"}}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v0 \
+    --run DQN \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_workers": 2}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v0 \
+    --run APEX \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_workers": 2, "timesteps_per_iteration": 1000, "num_gpus": 0, "min_iter_time_s": 1}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env FrozenLake-v0 \
+    --run DQN \
+    --stop '{"training_iteration": 2}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env FrozenLake-v0 \
+    --run PPO \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_sgd_iter": 10, "sgd_minibatch_size": 64, "train_batch_size": 1000, "num_workers": 1}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env PongDeterministic-v4 \
+    --run DQN \
+    --stop '{"training_iteration": 2}' \
+    --config '{"lr": 1e-4, "schedule_max_timesteps": 2000000, "buffer_size": 10000, "exploration_fraction": 0.1, "exploration_final_eps": 0.01, "sample_batch_size": 4, "learning_starts": 10000, "target_network_update_freq": 1000, "gamma": 0.99, "prioritized_replay": true}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env MontezumaRevenge-v0 \
+    --run PPO \
+    --stop '{"training_iteration": 2}' \
+    --config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"dim": 40, "conv_filters": [[16, [8, 8], 4], [32, [4, 4], 2], [512, [5, 5], 1]]}}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v1 \
+    --run A3C \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_workers": 2, "model": {"use_lstm": true}}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v0 \
+    --run DQN \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_workers": 2}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v0 \
+    --run PG \
+    --stop '{"training_iteration": 2}' \
+    --config '{"sample_batch_size": 500, "num_workers": 1}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v0 \
+    --run PG \
+    --stop '{"training_iteration": 2}' \
+    --config '{"sample_batch_size": 500, "use_pytorch": true}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v0 \
+    --run PG \
+    --stop '{"training_iteration": 2}' \
+    --config '{"sample_batch_size": 500, "num_workers": 1, "model": {"use_lstm": true, "max_seq_len": 100}}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v0 \
+    --run PG \
+    --stop '{"training_iteration": 2}' \
+    --config '{"sample_batch_size": 500, "num_workers": 1, "num_envs_per_worker": 10}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env Pong-v0 \
+    --run PG \
+    --stop '{"training_iteration": 2}' \
+    --config '{"sample_batch_size": 500, "num_workers": 1}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env FrozenLake-v0 \
+    --run PG \
+    --stop '{"training_iteration": 2}' \
+    --config '{"sample_batch_size": 500, "num_workers": 1}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env Pendulum-v0 \
+    --run DDPG \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_workers": 1}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v0 \
+    --run IMPALA \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v0 \
+    --run IMPALA \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "model": {"use_lstm": true}}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v0 \
+    --run IMPALA \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v0 \
+    --run IMPALA \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0, "model": {"use_lstm": true}}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env MountainCarContinuous-v0 \
+    --run DDPG \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_workers": 1}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env MountainCarContinuous-v0 \
+    --run DDPG \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_workers": 1}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env Pendulum-v0 \
+    --run APEX_DDPG \
+    --ray-num-cpus 8 \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env Pendulum-v0 \
+    --run APEX_DDPG \
+    --ray-num-cpus 8 \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1, "batch_mode": "complete_episodes", "parameter_noise": true}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v0 \
+    --run MARWIL \
+    --stop '{"training_iteration": 2}' \
+    --config '{"input": "/ray/python/ray/rllib/test/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "shuffle_buffer_size": 10}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v0 \
+    --run DQN \
+    --stop '{"training_iteration": 2}' \
+    --config '{"input": "/ray/python/ray/rllib/test/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "soft_q": true}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh test/test_local.py
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh test/test_io.py
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh test/test_checkpoint_restore.py
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh test/test_policy_evaluator.py
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh test/test_nested_spaces.py
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh test/test_external_env.py
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/parametric_action_cartpole.py --run=PG --stop=50
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/parametric_action_cartpole.py --run=PPO --stop=50
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/parametric_action_cartpole.py --run=DQN --stop=50
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh test/test_lstm.py
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/batch_norm_model.py --num-iters=1 --run=PPO
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/batch_norm_model.py --num-iters=1 --run=PG
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/batch_norm_model.py --num-iters=1 --run=DQN
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/batch_norm_model.py --num-iters=1 --run=DDPG
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh test/test_multi_agent_env.py
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh test/test_supported_spaces.py
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    pytest /ray/python/ray/tune/test/cluster_tests.py
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh test/test_env_with_subprocess.py
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh test/test_rollout.sh
+
+# Run all single-agent regression tests (3x retry each)
+for yaml in $(ls $ROOT_DIR/../../python/ray/rllib/tuned_examples/regression_tests); do
+    docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+        /ray/python/ray/rllib/test/run_silent.sh test/run_regression_tests.py \
+            /ray/python/ray/rllib/tuned_examples/regression_tests/$yaml
+done
+
+# Try a couple times since it's stochastic
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+        /ray/python/ray/rllib/test/run_silent.sh test/multiagent_pendulum.py || \
+    docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+        /ray/python/ray/rllib/test/run_silent.sh test/multiagent_pendulum.py || \
+    docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+        /ray/python/ray/rllib/test/run_silent.sh test/multiagent_pendulum.py
+
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/multiagent_cartpole.py --num-iters=2
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/multiagent_two_trainers.py --num-iters=2
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh test/test_avail_actions_qmix.py
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/cartpole_lstm.py --run=PPO --stop=200
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/cartpole_lstm.py --run=IMPALA --stop=100
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/cartpole_lstm.py --stop=200 --use-prev-action-reward
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/custom_metrics_and_callbacks.py --num-iters=2
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh contrib/random_agent/random_agent.py
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/twostep_game.py --stop=2000 --run=PG
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/twostep_game.py --stop=2000 --run=QMIX
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh examples/twostep_game.py --stop=2000 --run=APEX_QMIX
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env PongDeterministic-v4 \
+    --run A3C \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_workers": 2, "use_pytorch": true, "sample_async": false, "model": {"use_lstm": false, "grayscale": true, "zero_mean": false, "dim": 84}, "preprocessor_pref": "rllib"}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env CartPole-v1 \
+    --run A3C \
+    --stop '{"training_iteration": 2}' \
+    --config '{"num_workers": 2, "use_pytorch": true, "sample_async": false}'
+
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/python/ray/rllib/test/run_silent.sh train.py \
+    --env PongDeterministic-v4 \
+    --run IMPALA \
+    --stop='{"timesteps_total": 40000}' \
+    --ray-object-store-memory=500000000 \
+    --config '{"num_workers": 1, "num_gpus": 0, "num_envs_per_worker": 64, "sample_batch_size": 50, "train_batch_size": 50, "learner_queue_size": 1}'
diff --git a/python/ray/rllib/test/run_silent.sh b/python/ray/rllib/test/run_silent.sh
new file mode 100755
index 000000000..e0a69dd7b
--- /dev/null
+++ b/python/ray/rllib/test/run_silent.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+
+TMPFILE=`mktemp`
+DIRECTORY=`dirname $0`
+SCRIPT=$1
+shift
+
+if [ -x $DIRECTORY/../$SCRIPT ]; then
+    $DIRECTORY/../$SCRIPT "$@" >$TMPFILE 2>&1
+else
+    python $DIRECTORY/../$SCRIPT "$@" >$TMPFILE 2>&1
+fi
+
+CODE=$?
+if [ $CODE != 0 ]; then
+    cat $TMPFILE
+    echo "FAILED $CODE"
+    exit $CODE
+fi
+
+exit 0