1d4823c0ec446e93d00df8ca654db4b45b63b3d4,rllib/agents/dqn/tests/test_dqn.py,TestDQN,test_dqn_compilation,#TestDQN#,13

Before Change

            print(results)

        // tf.
        tf_config = config.copy()
        tf_config["eager"] = False
        trainer = dqn.DQNTrainer(config=tf_config, env="CartPole-v0")
        num_iterations = 1
        for i in range(num_iterations):
            results = trainer.train()
            print(results)

After Change

        config["num_workers"] = 0  // Run locally.
        num_iterations = 2

        for _ in framework_iterator(config, frameworks=["tf", "eager"]):
            // Rainbow.
            rainbow_config = config.copy()
            rainbow_config["num_atoms"] = 10
            rainbow_config["noisy"] = True
            rainbow_config["double_q"] = True
            rainbow_config["dueling"] = True
            rainbow_config["n_step"] = 5
            trainer = dqn.DQNTrainer(config=rainbow_config, env="CartPole-v0")
            for i in range(num_iterations):
                results = trainer.train()
                print(results)

            // double-dueling DQN.
            plain_config = config.copy()
            trainer = dqn.DQNTrainer(config=plain_config, env="CartPole-v0")
            for i in range(num_iterations):
                results = trainer.train()
                print(results)

    def test_dqn_exploration_and_soft_q_config(self):
        Tests, whether a DQN Agent outputs exploration/softmaxed actions.
        config = dqn.DEFAULT_CONFIG.copy()
        config["num_workers"] = 0  // Run locally.

Instances