ray/rllib/tuned_examples/dqn/pong-dqn.yaml

# You can expect ~20 reward within 1.1m timesteps / 2.1 hours on a K80 GPU
pong-deterministic-dqn:
    env: PongDeterministic-v4
    run: DQN
    stop:
        episode_reward_mean: 20
        time_total_s: 7200
    config:
        # Works for both torch and tf.
        framework: tf
        num_gpus: 1
        gamma: 0.99
        lr: .0001
        replay_buffer_config:
          type: MultiAgentPrioritizedReplayBuffer
          capacity: 50000
        num_steps_sampled_before_learning_starts: 10000
        rollout_fragment_length: 4
        train_batch_size: 32
        exploration_config:
          epsilon_timesteps: 200000
          final_epsilon: .01
        model:
          grayscale: True
          zero_mean: False
          dim: 42
        # we should set compress_observations to True because few machines
        # would be able to contain the replay buffers in memory otherwise
        compress_observations: True