feature(pu): polish chess env and its render method, add unittest and…

… configs (#272)

* feature(pu): polish chess env, its unittest, add configs

* feature(pu): add render option in chess env, polish human_to_action method

lzero/entry/train_alphazero.py

@@ -106,7 +106,7 @@ def train_alphazero(
         )
 
         # Evaluate policy performance
-        if evaluator.should_eval(learner.train_iter):
+        if evaluator.should_eval(learner.train_iter) and learner.train_iter > 0:
             stop, reward = evaluator.eval(
                 learner.save_checkpoint,
                 learner.train_iter,

lzero/policy/alphazero.py

@@ -364,6 +364,15 @@ def _get_simulation_env(self):
             else:
                 raise NotImplementedError
             self.simulate_env = Connect4Env(connect4_alphazero_config.env)
+        elif self._cfg.simulation_env_id == 'chess':
+            from zoo.board_games.chess.envs.chess_lightzero_env import ChessLightZeroEnv
+            if self._cfg.simulation_env_config_type == 'play_with_bot':
+                from zoo.board_games.chess.config.chess_alphazero_bot_mode_config import chess_alphazero_config
+            elif self._cfg.simulation_env_config_type == 'self_play':
+                from zoo.board_games.chess.config.chess_alphazero_sp_mode_config import chess_alphazero_config
+            else:
+                raise NotImplementedError
+            self.simulate_env = ChessLightZeroEnv(chess_alphazero_config.env)
         else:
             raise NotImplementedError
 

zoo/board_games/chess/config/chess_alphazero_bot_mode_config.py

@@ -0,0 +1,114 @@
+from easydict import EasyDict
+
+# ==============================================================
+# begin of the most frequently changed config specified by the user
+# ==============================================================
+# collector_env_num = 8
+# n_episode = 8
+# evaluator_env_num = 5
+# num_simulations = 400
+# update_per_collect = 200
+# batch_size = 512
+# max_env_step = int(1e6)
+# mcts_ctree = False
+
+# TODO: for debug
+collector_env_num = 2
+n_episode = 2
+evaluator_env_num = 2
+num_simulations = 4
+update_per_collect = 2
+batch_size = 2
+max_env_step = int(1e4)
+mcts_ctree = False
+# ==============================================================
+# end of the most frequently changed config specified by the user
+# ==============================================================
+
+chess_alphazero_config = dict(
+    exp_name=f'data_az_ptree/chess_alphazero_bot-mode_ns{num_simulations}_upc{update_per_collect}_seed0',
+    env=dict(
+        board_size=8,
+        battle_mode='play_with_bot_mode',
+        channel_last=False,
+        collector_env_num=collector_env_num,
+        evaluator_env_num=evaluator_env_num,
+        n_evaluator_episode=evaluator_env_num,
+        manager=dict(shared_memory=False, ),
+        # ==============================================================
+        # for the creation of simulation env
+        agent_vs_human=False,
+        prob_random_agent=0,
+        prob_expert_agent=0,
+        scale=True,
+        alphazero_mcts_ctree=mcts_ctree,
+        save_replay_gif=False,
+        replay_path_gif='./replay_gif',
+        # ==============================================================
+    ),
+    policy=dict(
+        mcts_ctree=mcts_ctree,
+        # ==============================================================
+        # for the creation of simulation env
+        simulation_env_id='chess',
+        simulation_env_config_type='play_with_bot',
+        # ==============================================================
+        model=dict(
+            observation_shape=(8, 8, 20),
+            action_space_size=int(8 * 8 * 73),
+            # TODO: for debug
+            num_res_blocks=1,
+            num_channels=1,
+            fc_value_layers=[16],
+            fc_policy_layers=[16],
+            # num_res_blocks=8,
+            # num_channels=256,
+            # fc_value_layers=[256, 256],
+            # fc_policy_layers=[256, 256],
+        ),
+        cuda=True,
+        board_size=8,
+        update_per_collect=update_per_collect,
+        batch_size=batch_size,
+        optim_type='AdamW',
+        lr_piecewise_constant_decay=False,
+        learning_rate=0.0001,
+        grad_clip_value=0.5,
+        value_weight=1.0,
+        entropy_weight=0.01,
+        n_episode=n_episode,
+        eval_freq=int(1e3),
+        mcts=dict(num_simulations=num_simulations),
+        collector_env_num=collector_env_num,
+        evaluator_env_num=evaluator_env_num,
+    ),
+)
+
+chess_alphazero_config = EasyDict(chess_alphazero_config)
+main_config = chess_alphazero_config
+
+chess_alphazero_create_config = dict(
+    env=dict(
+        type='chess_lightzero',
+        import_names=['zoo.board_games.chess.envs.chess_lightzero_env'],
+    ),
+    env_manager=dict(type='subprocess'),
+    policy=dict(
+        type='alphazero',
+        import_names=['lzero.policy.alphazero'],
+    ),
+    collector=dict(
+        type='episode_alphazero',
+        import_names=['lzero.worker.alphazero_collector'],
+    ),
+    evaluator=dict(
+        type='alphazero',
+        import_names=['lzero.worker.alphazero_evaluator'],
+    )
+)
+chess_alphazero_create_config = EasyDict(chess_alphazero_create_config)
+create_config = chess_alphazero_create_config
+
+if __name__ == '__main__':
+    from lzero.entry import train_alphazero
+    train_alphazero([main_config, create_config], seed=0, max_env_step=max_env_step)

zoo/board_games/chess/config/chess_alphazero_sp_mode_config.py

@@ -0,0 +1,113 @@
+from easydict import EasyDict
+
+# ==============================================================
+# begin of the most frequently changed config specified by the user
+# ==============================================================
+collector_env_num = 8
+n_episode = 8
+evaluator_env_num = 5
+num_simulations = 400
+update_per_collect = 200
+batch_size = 512
+max_env_step = int(1e6)
+mcts_ctree = True
+
+# TODO: for debug
+# collector_env_num = 2
+# n_episode = 2
+# evaluator_env_num = 2
+# num_simulations = 4
+# update_per_collect = 2
+# batch_size = 2
+# max_env_step = int(1e4)
+# mcts_ctree = False
+# ==============================================================
+# end of the most frequently changed config specified by the user
+# ==============================================================
+chess_alphazero_config = dict(
+    exp_name='data_az_ctree/chess_sp-mode_alphazero_seed0',
+    env=dict(
+        board_size=8,
+        battle_mode='self_play_mode',
+        channel_last=False,  
+        collector_env_num=collector_env_num,
+        evaluator_env_num=evaluator_env_num,
+        n_evaluator_episode=evaluator_env_num,
+        manager=dict(shared_memory=False, ),
+        # ==============================================================
+        # for the creation of simulation env
+        agent_vs_human=False,
+        prob_random_agent=0,
+        prob_expert_agent=0,
+        scale=True,
+        alphazero_mcts_ctree=mcts_ctree,
+        save_replay_gif=False,
+        replay_path_gif='./replay_gif',
+        # ==============================================================
+    ),
+    policy=dict(
+        mcts_ctree=mcts_ctree,
+        # ==============================================================
+        # for the creation of simulation env
+        simulation_env_id='chess',
+        simulation_env_config_type='self_play',  
+        # ==============================================================
+        model=dict(
+            observation_shape=(8, 8, 20),
+            action_space_size=int(8 * 8 * 73),
+            # TODO: for debug
+            num_res_blocks=1,
+            num_channels=1,
+            fc_value_layers=[16],
+            fc_policy_layers=[16],
+            # num_res_blocks=8,
+            # num_channels=256,
+            # fc_value_layers=[256, 256],
+            # fc_policy_layers=[256, 256],
+        ),
+        cuda=True,
+        board_size=8,
+        update_per_collect=update_per_collect,
+        batch_size=batch_size,
+        optim_type='AdamW',
+        lr_piecewise_constant_decay=False,
+        learning_rate=0.0001,
+        grad_clip_value=0.5,
+        value_weight=1.0,
+        entropy_weight=0.01,
+        n_episode=n_episode,
+        eval_freq=int(1e3),
+        mcts=dict(num_simulations=num_simulations),
+        collector_env_num=collector_env_num,
+        evaluator_env_num=evaluator_env_num,
+    ),
+)
+
+chess_alphazero_config = EasyDict(chess_alphazero_config)
+main_config = chess_alphazero_config
+
+chess_alphazero_create_config = dict(
+    env=dict(
+        type='chess_lightzero',
+        import_names=['zoo.board_games.chess.envs.chess_lightzero_env'],
+    ),
+    env_manager=dict(type='subprocess'),
+    policy=dict(
+        type='alphazero',
+        import_names=['lzero.policy.alphazero'],
+    ),
+    collector=dict(
+        type='episode_alphazero',
+        import_names=['lzero.worker.alphazero_collector'], 
+    ),
+    evaluator=dict(
+        type='alphazero',
+        import_names=['lzero.worker.alphazero_evaluator'],
+    )
+)
+chess_alphazero_create_config = EasyDict(chess_alphazero_create_config)
+create_config = chess_alphazero_create_config
+
+if __name__ == '__main__':
+    from lzero.entry import train_alphazero
+    train_alphazero([main_config, create_config], seed=0, max_env_step=max_env_step)