Merge branch 'master' into ttt_update

tutorials/SB3/connect_four/sb3_connect_four_action_mask.py

@@ -37,9 +37,23 @@ def reset(self, seed=None, options=None):
         return self.observe(self.agent_selection), {}
 
     def step(self, action):
-        """Gymnasium-like step function, returning observation, reward, termination, truncation, info."""
+        """Gymnasium-like step function, returning observation, reward, termination, truncation, info.
+
+        The observation is for the next agent (used to determine the next action), while the remaining
+        items are for the agent that just acted (used to understand what just happened).
+        """
+        current_agent = self.agent_selection
+
         super().step(action)
-        return super().last()
+
+        next_agent = self.agent_selection
+        return (
+            self.observe(next_agent),
+            self._cumulative_rewards[current_agent],
+            self.terminations[current_agent],
+            self.truncations[current_agent],
+            self.infos[current_agent],
+        )
 
     def observe(self, agent):
         """Return only raw observation, removing action mask."""

tutorials/SB3/test/test_sb3_action_mask.py

@@ -23,14 +23,14 @@
 EASY_ENVS = [
     gin_rummy_v4,
     texas_holdem_no_limit_v6,  # texas holdem human rendered game ends instantly, but with random actions it works fine
-    texas_holdem_v4,
     tictactoe_v3,
+    leduc_holdem_v4,
 ]
 
 # More difficult environments which will likely take more training time
 MEDIUM_ENVS = [
-    leduc_holdem_v4,  # with 10x as many steps it gets higher total rewards (9 vs -9), 0.52 winrate, and 0.92 vs 0.83 total scores
     hanabi_v5,  # even with 10x as many steps, total score seems to always be tied between the two agents
+    texas_holdem_v4,  # this performs poorly with updates to SB3 wrapper
     chess_v6,  # difficult to train because games take so long, performance varies heavily
 ]
 
@@ -50,10 +50,7 @@ def test_action_mask_easy(env_fn):
 
     env_kwargs = {}
 
-    steps = 8192
-    # These take slightly longer to outperform random
-    if env_fn in [leduc_holdem_v4, tictactoe_v3]:
-        steps *= 4
+    steps = 8192 * 4
 
     # Train a model against itself (takes ~2 minutes on GPU)
     train_action_mask(env_fn, steps=steps, seed=0, **env_kwargs)