(2023/11) merge develop into master

.github/workflows/action.yaml

@@ -12,7 +12,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: [3.7, 3.8, 3.9]
+        python-version: ['3.8', '3.9', '3.10']
     steps:
       - name: Checkout
         uses: actions/checkout@v2
@@ -24,7 +24,6 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install -r requirements.txt
-          pip install -r handyrl/envs/kaggle/requirements.txt
       - name: pytest
         run: |
           python -m pytest tests

handyrl/evaluation.py

@@ -106,7 +106,7 @@ def exec_match(env, agents, critic=None, show=False, game_args={}):
     outcome = env.outcome()
     if show:
         print('final outcome = %s' % outcome)
-    return outcome
+    return {'result': outcome}
 
 
 def exec_network_match(env, network_agents, critic=None, show=False, game_args={}):
@@ -138,7 +138,7 @@ def exec_network_match(env, network_agents, critic=None, show=False, game_args={
     outcome = env.outcome()
     for p, agent in network_agents.items():
         agent.outcome(outcome[p])
-    return outcome
+    return {'result': outcome}
 
 
 def build_agent(raw, env=None):
@@ -170,11 +170,11 @@ def execute(self, models, args):
             else:
                 agents[p] = Agent(model)
 
-        outcome = exec_match(self.env, agents)
-        if outcome is None:
+        results = exec_match(self.env, agents)
+        if results is None:
             print('None episode in evaluation!')
             return None
-        return {'args': args, 'result': outcome, 'opponent': opponent}
+        return {'args': args, 'opponent': opponent, **results}
 
 
 def wp_func(results):
@@ -196,10 +196,10 @@ def eval_process_mp_child(agents, critic, env_args, index, in_queue, out_queue,
         print('*** Game %d ***' % g)
         agent_map = {env.players()[p]: agents[ai] for p, ai in enumerate(agent_ids)}
         if isinstance(list(agent_map.values())[0], NetworkAgent):
-            outcome = exec_network_match(env, agent_map, critic, show=show, game_args=game_args)
+            results = exec_network_match(env, agent_map, critic, show=show, game_args=game_args)
         else:
-            outcome = exec_match(env, agent_map, critic, show=show, game_args=game_args)
-        out_queue.put((pat_idx, agent_ids, outcome))
+            results = exec_match(env, agent_map, critic, show=show, game_args=game_args)
+        out_queue.put((pat_idx, agent_ids, results))
     out_queue.put(None)
 
 
@@ -246,7 +246,8 @@ def evaluate_mp(env, agents, critic, env_args, args_patterns, num_process, num_g
         if ret is None:
             finished_cnt += 1
             continue
-        pat_idx, agent_ids, outcome = ret
+        pat_idx, agent_ids, results = ret
+        outcome = results.get('result')
         if outcome is not None:
             for idx, p in enumerate(env.players()):
                 agent_id = agent_ids[idx]

handyrl/train.py

@@ -63,24 +63,23 @@ def replace_none(a, b):
 
         # data that is changed by training configuration
         if args['turn_based_training'] and not args['observation']:
-            obs = [[m['observation'][m['turn'][0]]] for m in moments]
-            prob = np.array([[[m['selected_prob'][m['turn'][0]]]] for m in moments])
-            act = np.array([[m['action'][m['turn'][0]]] for m in moments], dtype=np.int64)[..., np.newaxis]
-            amask = np.array([[m['action_mask'][m['turn'][0]]] for m in moments])
+            players_list = [[m['turn'][0]] for m in moments]
         else:
-            obs = [[replace_none(m['observation'][player], obs_zeros) for player in players] for m in moments]
-            prob = np.array([[[replace_none(m['selected_prob'][player], 1.0)] for player in players] for m in moments])
-            act = np.array([[replace_none(m['action'][player], 0) for player in players] for m in moments], dtype=np.int64)[..., np.newaxis]
-            amask = np.array([[replace_none(m['action_mask'][player], amask_zeros + 1e32) for player in players] for m in moments])
+            players_list = [players for m in moments]
+
+        obs = [[replace_none(m['observation'][player], obs_zeros) for player in players_] for m, players_ in zip(moments, players_list)]
+        prob = np.array([[[replace_none(m['selected_prob'][player], 1.0)] for player in players_] for m, players_ in zip(moments, players_list)])
+        act = np.array([[replace_none(m['action'][player], 0) for player in players_] for m, players_ in zip(moments, players_list)], dtype=np.int64)[..., np.newaxis]
+        amask = np.array([[replace_none(m['action_mask'][player], amask_zeros + 1e32) for player in players_] for m, players_ in zip(moments, players_list)])
 
         # reshape observation
         obs = rotate(rotate(obs))  # (T, P, ..., ...) -> (P, ..., T, ...) -> (..., T, P, ...)
         obs = bimap_r(obs_zeros, obs, lambda _, o: np.array(o))
 
         # datum that is not changed by training configuration
-        v = np.array([[replace_none(m['value'][player], [0]) for player in players] for m in moments], dtype=np.float32).reshape(len(moments), len(players), -1)
-        rew = np.array([[replace_none(m['reward'][player], [0]) for player in players] for m in moments], dtype=np.float32).reshape(len(moments), len(players), -1)
-        ret = np.array([[replace_none(m['return'][player], [0]) for player in players] for m in moments], dtype=np.float32).reshape(len(moments), len(players), -1)
+        v = np.array([[replace_none(m['value'][player], 0) for player in players] for m in moments], dtype=np.float32).reshape(len(moments), len(players), -1)
+        rew = np.array([[replace_none(m['reward'][player], 0) for player in players] for m in moments], dtype=np.float32).reshape(len(moments), len(players), -1)
+        ret = np.array([[replace_none(m['return'][player], 0) for player in players] for m in moments], dtype=np.float32).reshape(len(moments), len(players), -1)
         oc = np.array([ep['outcome'][player] for player in players], dtype=np.float32).reshape(1, len(players), -1)
 
         emask = np.ones((len(moments), 1, 1), dtype=np.float32)  # episode mask

tests/test_environment.py

@@ -8,7 +8,7 @@
     'tictactoe',
     'geister',
     'parallel_tictactoe',
-    'kaggle.hungry_geese',
+    # 'kaggle.hungry_geese',
 ]