docs: Add ray_example

examples/ray_example.py

@@ -0,0 +1,36 @@
+from generals.agents import RandomAgent, ExpanderAgent
+from ray.rllib.algorithms.ppo import PPOConfig
+import gymnasium as gym
+from ray import tune
+from pprint import pprint
+
+#########################################################################################
+# Currently, it seems like RLLIB uses only gymnasium 0.x, but we support gymnasium 1.0+.#
+# Therefore this example may not always work.                                           #
+#########################################################################################
+
+
+def env_creator(env_config):
+    agent = RandomAgent() # Initialize your custom agent
+    npc = ExpanderAgent() # Initialize an NPC agent
+    env = gym.make("gym-generals-rllib-v0", agent=agent, npc=npc) # Create the environment
+    return env
+
+tune.register_env("generals_env", env_creator)
+
+config = (
+    PPOConfig()
+    .api_stack(
+    enable_rl_module_and_learner=True,
+    enable_env_runner_and_connector_v2=True,
+    )
+    .environment("generals_env") # Use the generals environment
+    .env_runners(num_env_runners=1)
+)
+
+algo = config.build()
+
+for i in range(10):
+    result = algo.train()
+    result.pop("config")
+    pprint(result)