Add many_ppo long running test (#12364)

* add new tes

* update

* update

release/long_running_tests/workloads/many_ppo.py

@@ -0,0 +1,49 @@
+# This workload tests running many instances of PPO (many actors)
+# This covers https://github.com/ray-project/ray/pull/12148
+
+import ray
+from ray.cluster_utils import Cluster
+from ray.tune import run_experiments
+
+num_redis_shards = 5
+redis_max_memory = 10**8
+object_store_memory = 10**9
+num_nodes = 3
+
+message = ("Make sure there is enough memory on this machine to run this "
+           "workload. We divide the system memory by 2 to provide a buffer.")
+assert (num_nodes * object_store_memory + num_redis_shards * redis_max_memory <
+        ray.utils.get_system_memory() / 2), message
+
+# Simulate a cluster on one machine.
+
+cluster = Cluster()
+for i in range(num_nodes):
+    cluster.add_node(
+        redis_port=6379 if i == 0 else None,
+        num_redis_shards=num_redis_shards if i == 0 else None,
+        num_cpus=20,
+        num_gpus=0,
+        resources={str(i): 2},
+        object_store_memory=object_store_memory,
+        redis_max_memory=redis_max_memory,
+        dashboard_host="0.0.0.0")
+ray.init(address=cluster.address)
+
+# Run the workload.
+
+run_experiments({
+    "ppo": {
+        "run": "PPO",
+        "env": "CartPole-v0",
+        "num_samples": 10000,
+        "config": {
+            "num_workers": 8,
+            "num_gpus": 0,
+            "num_sgd_iter": 1,
+        },
+        "stop": {
+            "timesteps_total": 1,
+        },
+    }
+})