[RLlib] Add HowTo set env seed to our custom env example script. (#14471)

rllib/examples/custom_env.py

@@ -12,10 +12,12 @@ import gym
 from gym.spaces import Discrete, Box
 import numpy as np
 import os
+import random
 
 import ray
 from ray import tune
 from ray.tune import grid_search
+from ray.rllib.env.env_context import EnvContext
 from ray.rllib.models import ModelCatalog
 from ray.rllib.models.tf.tf_modelv2 import TFModelV2
 from ray.rllib.models.tf.fcnet import FullyConnectedNetwork
@@ -41,12 +43,14 @@ class SimpleCorridor(gym.Env):
 
     You can configure the length of the corridor via the env config."""
 
-    def __init__(self, config):
+    def __init__(self, config: EnvContext):
         self.end_pos = config["corridor_length"]
         self.cur_pos = 0
         self.action_space = Discrete(2)
         self.observation_space = Box(
             0.0, self.end_pos, shape=(1, ), dtype=np.float32)
+        # Set the seed. This is only used for the final (reach goal) reward.
+        self.seed(config.worker_index * config.num_workers)
 
     def reset(self):
         self.cur_pos = 0
@@ -59,7 +63,12 @@ class SimpleCorridor(gym.Env):
         elif action == 1:
             self.cur_pos += 1
         done = self.cur_pos >= self.end_pos
-        return [self.cur_pos], 1.0 if done else -0.1, done, {}
+        # Produce a random reward when we reach the goal.
+        return [self.cur_pos], \
+            random.random() * 2 if done else -0.1, done, {}
+
+    def seed(self, seed=None):
+        random.seed(seed)
 
 
 class CustomModel(TFModelV2):