Claude/install rlcard e eb7h

.gitignore

@@ -15,5 +15,6 @@ docs/rst
 docs/sphinx
 experiments/
 dist/
+build/
 rlcard/games/doudizhu/jsondata/
 rlcard/agents/gin_rummy_human_agent/gui_cards/cards_png

pyproject.toml

@@ -0,0 +1,3 @@
+[build-system]
+requires = ["setuptools>=42", "wheel"]
+build-backend = "setuptools.build_meta"

setup.py

@@ -42,7 +42,7 @@ def _get_version():
         'termcolor'
     ],
     extras_require=extras,
-    requires_python='>=3.7',
+    python_requires='>=3.7',
     classifiers=[
         "Programming Language :: Python :: 3.11",
         "Programming Language :: Python :: 3.10",

train.py

@@ -0,0 +1,157 @@
+import os
+import argparse
+
+import torch
+
+import rlcard
+from rlcard.agents import RandomAgent
+from rlcard.utils import (
+    get_device,
+    set_seed,
+    tournament,
+    reorganize,
+    Logger,
+    plot_curve,
+)
+
+def train(args):
+
+    # Check whether gpu is available
+    device = get_device()
+
+    # Seed numpy, torch, random
+    set_seed(args.seed)
+
+    # Make the environment with seed
+    env = rlcard.make(
+        args.env,
+        config={
+            'seed': args.seed,
+        }
+    )
+
+    # Initialize the agent and use random agents as opponents
+    if args.algorithm == 'dqn':
+        from rlcard.agents import DQNAgent
+        agent = DQNAgent(
+            num_actions=env.num_actions,
+            state_shape=env.state_shape[0],
+            mlp_layers=[64,64],
+            device=device,
+        )
+    elif args.algorithm == 'nfsp':
+        from rlcard.agents import NFSPAgent
+        agent = NFSPAgent(
+            num_actions=env.num_actions,
+            state_shape=env.state_shape[0],
+            hidden_layers_sizes=[64,64],
+            q_mlp_layers=[64,64],
+            device=device,
+        )
+    agents = [agent]
+    for _ in range(1, env.num_players):
+        agents.append(RandomAgent(num_actions=env.num_actions))
+    env.set_agents(agents)
+
+    # Start training
+    with Logger(args.log_dir) as logger:
+        for episode in range(args.num_episodes):
+
+            if args.algorithm == 'nfsp':
+                agents[0].sample_episode_policy()
+
+            # Generate data from the environment
+            trajectories, payoffs = env.run(is_training=True)
+
+            # Reorganaize the data to be state, action, reward, next_state, done
+            trajectories = reorganize(trajectories, payoffs)
+
+            # Feed transitions into agent memory, and train the agent
+            # Here, we assume that DQN always plays the first position
+            # and the other players play randomly (if any)
+            for ts in trajectories[0]:
+                agent.feed(ts)
+
+            # Evaluate the performance. Play with random agents.
+            if episode % args.evaluate_every == 0:
+                logger.log_performance(
+                    episode,
+                    tournament(
+                        env,
+                        args.num_eval_games,
+                    )[0]
+                )
+
+        # Get the paths
+        csv_path, fig_path = logger.csv_path, logger.fig_path
+
+    # Plot the learning curve
+    plot_curve(csv_path, fig_path, args.algorithm)
+
+    # Save model
+    save_path = os.path.join(args.log_dir, 'model.pth')
+    torch.save(agent, save_path)
+    print('Model saved in', save_path)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser("DQN/NFSP example in RLCard")
+    parser.add_argument(
+        '--env',
+        type=str,
+        default='leduc-holdem',
+        choices=[
+            'blackjack',
+            'leduc-holdem',
+            'limit-holdem',
+            'doudizhu',
+            'mahjong',
+            'no-limit-holdem',
+            'uno',
+            'gin-rummy',
+            'bridge',
+        ],
+    )
+    parser.add_argument(
+        '--algorithm',
+        type=str,
+        default='dqn',
+        choices=[
+            'dqn',
+            'nfsp',
+        ],
+    )
+    parser.add_argument(
+        '--cuda',
+        type=str,
+        default='',
+    )
+    parser.add_argument(
+        '--seed',
+        type=int,
+        default=42,
+    )
+    parser.add_argument(
+        '--num_episodes',
+        type=int,
+        default=5000,
+    )
+    parser.add_argument(
+        '--num_eval_games',
+        type=int,
+        default=2000,
+    )
+    parser.add_argument(
+        '--evaluate_every',
+        type=int,
+        default=100,
+    )
+    parser.add_argument(
+        '--log_dir',
+        type=str,
+        default='experiments/leduc_holdem_dqn_result/',
+    )
+
+    args = parser.parse_args()
+
+    os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda
+    train(args)