diff --git a/supersuit/vector/markov_vector_wrapper.py b/supersuit/vector/markov_vector_wrapper.py
index e900e8a..598a908 100644
--- a/supersuit/vector/markov_vector_wrapper.py
+++ b/supersuit/vector/markov_vector_wrapper.py
@@ -92,9 +92,14 @@ def step(self, actions):
         infs = [infos.get(agent, {}) for agent in self.par_env.possible_agents]
 
         if env_done:
-            observations, infs = self.reset()
+            observations, reset_infs = self.reset()
         else:
             observations = self.concat_obs(observations)
+            # empty infos for reset infs
+            reset_infs = [{} for _ in range(len(self.par_env.possible_agents))]
+        # combine standard infos and reset infos
+        infs = [{**inf, **reset_inf} for inf, reset_inf in zip(infs, reset_infs)]
+
         assert (
             self.black_death or self.par_env.agents == self.par_env.possible_agents
         ), "MarkovVectorEnv does not support environments with varying numbers of active agents unless black_death is set to True"
diff --git a/test/test_vector/test_pettingzoo_to_vec.py b/test/test_vector/test_pettingzoo_to_vec.py
index 9202a1b..bd379e7 100644
--- a/test/test_vector/test_pettingzoo_to_vec.py
+++ b/test/test_vector/test_pettingzoo_to_vec.py
@@ -1,5 +1,6 @@
 import copy
 
+import numpy as np
 import pytest
 from pettingzoo.butterfly import knights_archers_zombies_v10
 from pettingzoo.mpe import simple_spread_v3, simple_world_comm_v3
@@ -89,3 +90,28 @@ def test_env_black_death_wrapper():
     for i in range(300):
         actions = [env.action_space.sample() for i in range(env.num_envs)]
         obss, rews, terms, truncs, infos = env.step(actions)
+
+
+def test_terminal_obs_are_returned():
+    """
+    If we reach (and pass) the end of the episode, the last observation is returned in the info dict.
+    """
+    max_cycles = 300
+    env = knights_archers_zombies_v10.parallel_env(spawn_rate=50, max_cycles=300)
+    env = black_death_v3(env)
+    env = pettingzoo_env_to_vec_env_v1(env)
+    env.reset(seed=42)
+
+    # run past max_cycles or until terminated - causing the env to reset and continue
+    for _ in range(0, max_cycles + 10):
+        actions = [env.action_space.sample() for i in range(env.num_envs)]
+        _, _, terms, truncs, infos = env.step(actions)
+
+        env_done = (np.array(terms) | np.array(truncs)).all()
+
+        if env_done:
+            # check we have infos for all agents
+            assert len(infos) == len(env.par_env.possible_agents)
+            # check infos contain terminal_observation
+            for info in infos:
+                assert "terminal_observation" in info