ray/rllib/tuned_examples/a3c/cartpole-a2c-microbatch.yaml

cartpole-a2c-microbatch:
    env: CartPole-v0
    run: A2C
    stop:
        episode_reward_mean: 150
        timesteps_total: 100000
    config:
        # Works for both torch and tf.
        use_pytorch: false
        num_workers: 1
        gamma: 0.95
        microbatch_size: 50
        train_batch_size: 100
[RLlib] Benchmark and regression test yaml cleanup and restructuring. (#8414) 2020-05-26 11:10:27 +02:00			`cartpole-a2c-microbatch:`
[rllib] Add microbatch optimizer with A2C example (#6161) 2019-11-14 12:14:00 -08:00			`env: CartPole-v0`
			`run: A2C`
			`stop:`
[RLlib] Benchmark and regression test yaml cleanup and restructuring. (#8414) 2020-05-26 11:10:27 +02:00			`episode_reward_mean: 150`
[rllib] Add microbatch optimizer with A2C example (#6161) 2019-11-14 12:14:00 -08:00			`timesteps_total: 100000`
			`config:`
[RLlib] Benchmark and regression test yaml cleanup and restructuring. (#8414) 2020-05-26 11:10:27 +02:00			`# Works for both torch and tf.`
[RLlib] Stabilize Pendulum-v0 regression test cases. (#8232) Stabilize Pendulum regression test cases. 2020-04-30 15:48:11 +02:00			`use_pytorch: false`
[rllib] Add microbatch optimizer with A2C example (#6161) 2019-11-14 12:14:00 -08:00			`num_workers: 1`
			`gamma: 0.95`
			`microbatch_size: 50`
			`train_batch_size: 100`