Add example from README (with minor updates)

examples/discrete/discrete_dqn.py

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+import gymnasium as gym
+import torch
+from torch.utils.tensorboard import SummaryWriter
+
+import tianshou as ts
+
+
+def main():
+    task = "CartPole-v1"
+    lr, epoch, batch_size = 1e-3, 10, 64
+    train_num, test_num = 10, 100
+    gamma, n_step, target_freq = 0.9, 3, 320
+    buffer_size = 20000
+    eps_train, eps_test = 0.1, 0.05
+    step_per_epoch, step_per_collect = 10000, 10
+    logger = ts.utils.TensorboardLogger(SummaryWriter("log/dqn"))  # TensorBoard is supported!
+    # For other loggers: https://tianshou.readthedocs.io/en/master/tutorials/logger.html
+
+    # you can also try with SubprocVectorEnv
+    train_envs = ts.env.DummyVectorEnv([lambda: gym.make(task) for _ in range(train_num)])
+    test_envs = ts.env.DummyVectorEnv([lambda: gym.make(task) for _ in range(test_num)])
+
+    from tianshou.utils.net.common import Net
+
+    # you can define other net by following the API:
+    # https://tianshou.readthedocs.io/en/master/tutorials/dqn.html#build-the-network
+    env = gym.make(task, render_mode="human")
+    state_shape = env.observation_space.shape or env.observation_space.n
+    action_shape = env.action_space.shape or env.action_space.n
+    net = Net(state_shape=state_shape, action_shape=action_shape, hidden_sizes=[128, 128, 128])
+    optim = torch.optim.Adam(net.parameters(), lr=lr)
+
+    policy = ts.policy.DQNPolicy(
+        model=net,
+        optim=optim,
+        discount_factor=gamma,
+        action_space=env.action_space,
+        estimation_step=n_step,
+        target_update_freq=target_freq,
+    )
+    train_collector = ts.data.Collector(
+        policy,
+        train_envs,
+        ts.data.VectorReplayBuffer(buffer_size, train_num),
+        exploration_noise=True,
+    )
+    test_collector = ts.data.Collector(
+        policy,
+        test_envs,
+        exploration_noise=True,
+    )  # because DQN uses epsilon-greedy method
+
+    result = ts.trainer.OffpolicyTrainer(
+        policy=policy,
+        train_collector=train_collector,
+        test_collector=test_collector,
+        max_epoch=epoch,
+        step_per_epoch=step_per_epoch,
+        step_per_collect=step_per_collect,
+        episode_per_test=test_num,
+        batch_size=batch_size,
+        update_per_step=1 / step_per_collect,
+        train_fn=lambda epoch, env_step: policy.set_eps(eps_train),
+        test_fn=lambda epoch, env_step: policy.set_eps(eps_test),
+        stop_fn=lambda mean_rewards: mean_rewards >= env.spec.reward_threshold,
+        logger=logger,
+    ).run()
+    print(f"Finished training in {result.timing.total_time} seconds")
+
+    # watch performance
+    policy.eval()
+    policy.set_eps(eps_test)
+    collector = ts.data.Collector(policy, env, exploration_noise=True)
+    collector.collect(n_episode=100, render=1 / 35)
+
+
+if __name__ == "__main__":
+    main()