ray/rllib/tuned_examples/regression_tests/pendulum-ppo-tf.yaml

pendulum-ppo-tf:
    env: Pendulum-v0
    run: PPO
    stop:
        episode_reward_mean: -500
        timesteps_total: 400000
    config:
        use_pytorch: false
        train_batch_size: 2048
        vf_clip_param: 10.0
        num_workers: 0
        num_envs_per_worker: 10
        lambda: 0.1
        gamma: 0.95
        lr: 0.0003
        sgd_minibatch_size: 64
        num_sgd_iter: 10
        model:
            fcnet_hiddens: [64, 64]
        batch_mode: complete_episodes
        observation_filter: MeanStdFilter
[RLlib] Stabilize Pendulum-v0 regression test cases. (#8232) Stabilize Pendulum regression test cases. 2020-04-30 15:48:11 +02:00			`pendulum-ppo-tf:`
[rllib] Add more regression tests and autogenerate (#2324) 2018-07-02 08:20:53 -07:00			`env: Pendulum-v0`
			`run: PPO`
			`stop:`
[RLlib] Stabilize Pendulum-v0 regression test cases. (#8232) Stabilize Pendulum regression test cases. 2020-04-30 15:48:11 +02:00			`episode_reward_mean: -500`
			`timesteps_total: 400000`
[rllib] Add more regression tests and autogenerate (#2324) 2018-07-02 08:20:53 -07:00			`config:`
[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] clarify train batch size for PPO (#2793) It's possible to configure PPO in a way that ends up discarding most of the samples (they are treated as "stragglers"). Add a warning when this happens, and raise an exception if the waste is particularly egregious. 2018-09-05 12:06:13 -07:00			`train_batch_size: 2048`
[rllib] Add vf clipping param to fix pendulum example (#2921) * add vf clip * fix test * Update ppo.py 2018-09-23 13:11:17 -07:00			`vf_clip_param: 10.0`
[rllib] Run simple regressions tests for all algs in jenkins (#3498) 2018-12-11 17:21:53 -08:00			`num_workers: 0`
			`num_envs_per_worker: 10`
[rllib] Add more regression tests and autogenerate (#2324) 2018-07-02 08:20:53 -07:00			`lambda: 0.1`
			`gamma: 0.95`
[rllib] clarify train batch size for PPO (#2793) It's possible to configure PPO in a way that ends up discarding most of the samples (they are treated as "stragglers"). Add a warning when this happens, and raise an exception if the waste is particularly egregious. 2018-09-05 12:06:13 -07:00			`lr: 0.0003`
			`sgd_minibatch_size: 64`
[rllib] Add more regression tests and autogenerate (#2324) 2018-07-02 08:20:53 -07:00			`num_sgd_iter: 10`
			`model:`
			`fcnet_hiddens: [64, 64]`
[rllib] Default to truncate_episodes and add some more config validators (#2967) * update * link it * warn about truncation * fix * Update rllib-training.rst * deprecate tests failing 2018-09-30 18:37:55 -07:00			`batch_mode: complete_episodes`
[rllib] Set PPO observation filter to NoFilter by default (#4191) 2019-03-01 13:19:33 -08:00			`observation_filter: MeanStdFilter`