ray/rllib/tuned_examples/apex_dqn/cartpole-apex-dqn-fake-gpus.yaml

# Note here that with < 3 workers, APEX can behave a little unstably
# due to the (static) per-worker-epsilon distribution, which also makes
# evaluation w/o evaluation worker set harder.
# For an epsilon-free/greedy evaluation, use:
# evaluation_interval: 1
# evaluation_config:
#     explore: False
cartpole-apex-dqn:
    env: CartPole-v0
    run: APEX
    stop:
        episode_reward_mean: 150.0
        timesteps_total: 250000
    config:
        # Works for both torch and tf.
        framework: tf
        num_workers: 3
        optimizer:
            num_replay_buffer_shards: 1

        # Fake 2 GPUs.
        num_gpus: 2
        _fake_gpus: true