diff --git a/examples/ray_example.py b/examples/ray_example.py
deleted file mode 100644
index 65af950..0000000
--- a/examples/ray_example.py
+++ /dev/null
@@ -1,36 +0,0 @@
-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)
diff --git a/examples/record_replay_example.py b/examples/record_replay_example.py
index 8b0bcde..a3485c7 100644
--- a/examples/record_replay_example.py
+++ b/examples/record_replay_example.py
@@ -1,6 +1,4 @@
-import gymnasium as gym
-
-from generals import GridFactory
+from generals import GridFactory, PettingZooGenerals
 from generals.agents import RandomAgent, ExpanderAgent
 
 # Initialize agents
@@ -17,11 +15,15 @@
     # seed=38,  # Seed to generate the same map every time
 )
 
-env = gym.make(
-    "gym-generals-v0",  # Environment name
-    grid_factory=grid_factory,  # Grid factory
-    agent=agent,
-    npc=npc,  # NPC that will play against the agent
+agents ={
+    npc.id: npc,
+    agent.id: agent
+}
+
+env = PettingZooGenerals(
+    agents=[npc.id, agent.id],
+    grid_factory=grid_factory,
+    render_mode=None
 )
 
 # Options are used only for the next game
@@ -29,10 +31,12 @@
     "replay_file": "my_replay",  # Save replay as my_replay.pkl
 }
 
-observation, info = env.reset(options=options)
-
+observations, info = env.reset(options=options)
 terminated = truncated = False
 while not (terminated or truncated):
-    action = agent.act(observation)
-    observation, reward, terminated, truncated, info = env.step(action)
-    env.render()
+    actions = {}
+    for agent in env.agents:
+        # Ask agent for action
+        actions[agent] = agents[agent].act(observations[agent])
+    # All agents perform their actions
+    observations, rewards, terminated, truncated, info = env.step(actions)
diff --git a/generals/envs/gymnasium_generals.py b/generals/envs/gymnasium_generals.py
index ae46ee0..7f93a32 100644
--- a/generals/envs/gymnasium_generals.py
+++ b/generals/envs/gymnasium_generals.py
@@ -67,7 +67,8 @@ def __init__(
 
         # Initialize game state
         self.prior_observations: dict[str, Observation] | None = None
-        self.game = self._create_new_game()
+        grid = self.grid_factory.generate()
+        self.game = Game(grid, self.agents)
 
         # Set up spaces
         self.observation_space = self._create_observation_space()
@@ -78,11 +79,6 @@ def _setup_agent_data(self) -> dict[str, dict[str, Any]]:
         colors = [(255, 107, 108), (0, 130, 255)]
         return {id: {"color": color} for id, color in zip(self.agents, colors)}
 
-    def _create_new_game(self) -> Game:
-        """Create a new game instance."""
-        grid = self.grid_factory.generate()
-        return Game(grid, self.agents)
-
     def _create_observation_space(self) -> spaces.Space:
         """Create the observation space based on grid dimensions."""
         dim = self.pad_observations_to