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vpg-CartPole-v1

Reinforcement Learning
rl-algo-impls
CartPole-v1
vpg
deep-reinforcement-learning
Eval Results
Model card Files Community
vpg-CartPole-v1
File size: 3,568 Bytes 
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CartPole-v1: &cartpole-defaults
  n_timesteps: !!float 4e5
  algo_hyperparams:
    n_steps: 4096
    pi_lr: 0.01
    gamma: 0.99
    gae_lambda: 1
    val_lr: 0.01
    train_v_iters: 80
  eval_params:
    step_freq: !!float 2.5e4

CartPole-v0:
  <<: *cartpole-defaults
  n_timesteps: !!float 1e5
  algo_hyperparams:
    n_steps: 1024
    pi_lr: 0.01
    gamma: 0.99
    gae_lambda: 1
    val_lr: 0.01
    train_v_iters: 80

MountainCar-v0:
  n_timesteps: !!float 1e6
  env_hyperparams:
    normalize: true
    n_envs: 16
  algo_hyperparams:
    n_steps: 200
    pi_lr: 0.005
    gamma: 0.99
    gae_lambda: 0.97
    val_lr: 0.01
    train_v_iters: 80
    max_grad_norm: 0.5

MountainCarContinuous-v0:
  n_timesteps: !!float 3e5
  env_hyperparams:
    normalize: true
    n_envs: 4
  # policy_hyperparams:
  #   init_layers_orthogonal: false
  #   log_std_init: -3.29
  #   use_sde: true
  algo_hyperparams:
    n_steps: 1000
    pi_lr: !!float 5e-4
    gamma: 0.99
    gae_lambda: 0.9
    val_lr: !!float 1e-3
    train_v_iters: 80
    max_grad_norm: 5
  eval_params:
    step_freq: 5000

Acrobot-v1:
  n_timesteps: !!float 2e5
  algo_hyperparams:
    n_steps: 2048
    pi_lr: 0.005
    gamma: 0.99
    gae_lambda: 0.97
    val_lr: 0.01
    train_v_iters: 80
    max_grad_norm: 0.5

LunarLander-v2:
  n_timesteps: !!float 4e6
  policy_hyperparams:
    hidden_sizes: [256, 256]
  algo_hyperparams:
    n_steps: 2048
    pi_lr: 0.0001
    gamma: 0.999
    gae_lambda: 0.97
    val_lr: 0.0001
    train_v_iters: 80
    max_grad_norm: 0.5
  eval_params:
    deterministic: false

CarRacing-v0:
  n_timesteps: !!float 4e6
  env_hyperparams:
    frame_stack: 4
    n_envs: 4
    vec_env_class: "dummy"
  policy_hyperparams:
    use_sde: true
    log_std_init: -2
    init_layers_orthogonal: false
    activation_fn: relu
    cnn_feature_dim: 256
    hidden_sizes: [256]
  algo_hyperparams:
    n_steps: 1000
    pi_lr: !!float 5e-5
    gamma: 0.99
    gae_lambda: 0.95
    val_lr: !!float 1e-4
    train_v_iters: 40
    max_grad_norm: 0.5
    sde_sample_freq: 4

HalfCheetahBulletEnv-v0: &pybullet-defaults
  n_timesteps: !!float 2e6
  policy_hyperparams: &pybullet-policy-defaults
    hidden_sizes: [256, 256]
  algo_hyperparams: &pybullet-algo-defaults
    n_steps: 4000
    pi_lr: !!float 3e-4
    gamma: 0.99
    gae_lambda: 0.97
    val_lr: !!float 1e-3
    train_v_iters: 80
    max_grad_norm: 0.5

AntBulletEnv-v0:
  <<: *pybullet-defaults
  policy_hyperparams:
    <<: *pybullet-policy-defaults
    hidden_sizes: [400, 300]
  algo_hyperparams:
    <<: *pybullet-algo-defaults
    pi_lr: !!float 7e-4
    val_lr: !!float 7e-3

HopperBulletEnv-v0:
  <<: *pybullet-defaults

Walker2DBulletEnv-v0:
  <<: *pybullet-defaults

FrozenLake-v1:
  n_timesteps: !!float 8e5
  env_params:
    make_kwargs:
      map_name: 8x8
      is_slippery: true
  policy_hyperparams:
    hidden_sizes: [64]
  algo_hyperparams:
    n_steps: 2048
    pi_lr: 0.01
    gamma: 0.99
    gae_lambda: 0.98
    val_lr: 0.01
    train_v_iters: 80
    max_grad_norm: 0.5
  eval_params:
    step_freq: !!float 5e4
    n_episodes: 10
    save_best: true

_atari: &atari-defaults
  n_timesteps: !!float 1e7
  env_hyperparams:
    n_envs: 4
    frame_stack: 4
    no_reward_timeout_steps: 1000
    no_reward_fire_steps: 500
    vec_env_class: subproc
  policy_hyperparams:
    activation_fn: relu
  algo_hyperparams:
    n_steps: 2048
    pi_lr: !!float 1e-4
    gamma: 0.99
    gae_lambda: 0.95
    val_lr: !!float 2e-4
    train_v_iters: 80
    max_grad_norm: 0.5
  eval_params:
    deterministic: false