[rllib] Revert [rllib] Port DDPG to the build_tf_policy pattern (ray-…

…project#5626)

python/ray/ray_constants.py

@@ -54,7 +54,7 @@ def env_integer(key, default):
 # The maximum resource quantity that is allowed. TODO(rkn): This could be
 # relaxed, but the current implementation of the node manager will be slower
 # for large resource quantities due to bookkeeping of specific resource IDs.
-MAX_RESOURCE_QUANTITY = 10000
+MAX_RESOURCE_QUANTITY = 20000
 
 # Each memory "resource" counts as this many bytes of memory.
 MEMORY_RESOURCE_UNIT_BYTES = 50 * 1024 * 1024

rllib/agents/ddpg/ddpg.py

@@ -41,7 +41,7 @@
     # === Model ===
     # Apply a state preprocessor with spec given by the "model" config option
     # (like other RL algorithms). This is mostly useful if you have a weird
-    # observation shape, like an image. Auto-enabled if a custom model is set.
+    # observation shape, like an image. Disabled by default.
     "use_state_preprocessor": False,
     # Postprocess the policy network model output with these hidden layers. If
     # use_state_preprocessor is False, then these will be the *only* hidden
@@ -173,7 +173,7 @@ def make_exploration_schedule(config, worker_index):
     if config["per_worker_exploration"]:
         assert config["num_workers"] > 1, "This requires multiple workers"
         if worker_index >= 0:
-            # Exploration constants from the Ape-X paper
+            # FIXME: what do magic constants mean? (0.4, 7)
             max_index = float(config["num_workers"] - 1)
             exponent = 1 + worker_index / max_index * 7
             return ConstantSchedule(0.4**exponent)