ray/rllib/tuned_examples/dqn/cartpole-apex.yaml

# Should reach 100+ reward in 100k-200k ts.
# Note here that with 2 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: 100
        timesteps_total: 200000
    config:
        # Works for both torch and tf.
        framework: tf

        # Make this work with only 5 CPUs and 0 GPUs:
        num_workers: 2
        optimizer:
            num_replay_buffer_shards: 2
        num_gpus: 0

        min_iter_time_s: 5
        target_network_update_freq: 500
        learning_starts: 1000
        timesteps_per_iteration: 1000
        buffer_size: 20000

        model:
            fcnet_hiddens: [64]
            fcnet_activation: linear
        n_step: 3