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ppo-CarRacing-v0

Reinforcement Learning
rl-algo-impls
CarRacing-v0
ppo
deep-reinforcement-learning
Eval Results
Model card Files Community
ppo-CarRacing-v0
File size: 4,153 Bytes 
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CartPole-v1: &cartpole-defaults
  n_timesteps: !!float 1e5
  env_hyperparams:
    n_envs: 8
  algo_hyperparams:
    n_steps: 32
    batch_size: 256
    n_epochs: 20
    gae_lambda: 0.8
    gamma: 0.98
    ent_coef: 0.0
    learning_rate: 0.001
    learning_rate_decay: linear
    clip_range: 0.2
    clip_range_decay: linear
  eval_params:
    step_freq: !!float 2.5e4

CartPole-v0:
  <<: *cartpole-defaults
  n_timesteps: !!float 5e4

MountainCar-v0:
  n_timesteps: !!float 1e6
  env_hyperparams:
    normalize: true
    n_envs: 16
  algo_hyperparams:
    n_steps: 16
    n_epochs: 4
    gae_lambda: 0.98
    gamma: 0.99
    ent_coef: 0.0

MountainCarContinuous-v0:
  n_timesteps: !!float 1e5
  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: 512
    batch_size: 256
    n_epochs: 10
    learning_rate: !!float 7.77e-5
    ent_coef: 0.01 # 0.00429
    ent_coef_decay: linear
    clip_range: 0.1
    gae_lambda: 0.9
    max_grad_norm: 5
    vf_coef: 0.19
  eval_params:
    step_freq: 5000

Acrobot-v1:
  n_timesteps: !!float 1e6
  env_hyperparams:
    n_envs: 16
    normalize: true
  algo_hyperparams:
    n_steps: 256
    n_epochs: 4
    gae_lambda: 0.94
    gamma: 0.99
    ent_coef: 0.0

LunarLander-v2:
  n_timesteps: !!float 1e6
  env_hyperparams:
    n_envs: 16
  algo_hyperparams:
    n_steps: 1024
    batch_size: 64
    n_epochs: 4
    gae_lambda: 0.98
    gamma: 0.999
    ent_coef: 0.01
    ent_coef_decay: linear
    normalize_advantage: false

CarRacing-v0:
  n_timesteps: !!float 4e6
  env_hyperparams:
    n_envs: 8
    frame_stack: 4
  policy_hyperparams:
    use_sde: true
    log_std_init: -2
    init_layers_orthogonal: false
    activation_fn: relu
    share_features_extractor: false
    cnn_feature_dim: 256
    hidden_sizes: [256]
  algo_hyperparams:
    n_steps: 512
    batch_size: 128
    n_epochs: 10
    learning_rate: !!float 1e-4
    learning_rate_decay: linear
    gamma: 0.99
    gae_lambda: 0.95
    ent_coef: 0.0
    sde_sample_freq: 4
    max_grad_norm: 0.5
    vf_coef: 0.5
    clip_range: 0.2

# BreakoutNoFrameskip-v4
# PongNoFrameskip-v4
# SpaceInvadersNoFrameskip-v4
# QbertNoFrameskip-v4
atari: &atari-defaults
  n_timesteps: !!float 1e7
  policy_hyperparams:
    activation_fn: relu
  env_hyperparams: &atari-env-defaults
    n_envs: 8
    frame_stack: 4
    no_reward_timeout_steps: 1000
    no_reward_fire_steps: 500
    vec_env_class: subproc
  algo_hyperparams:
    n_steps: 128
    batch_size: 256
    n_epochs: 4
    learning_rate: !!float 2.5e-4
    learning_rate_decay: linear
    clip_range: 0.1
    clip_range_decay: linear
    vf_coef: 0.5
    ent_coef: 0.01
  eval_params:
    deterministic: false

HalfCheetahBulletEnv-v0: &pybullet-defaults
  n_timesteps: !!float 2e6
  env_hyperparams: &pybullet-env-defaults
    n_envs: 16
    normalize: true
  policy_hyperparams: &pybullet-policy-defaults
    pi_hidden_sizes: [256, 256]
    v_hidden_sizes: [256, 256]
    activation_fn: relu
  algo_hyperparams: &pybullet-algo-defaults
    n_steps: 512
    batch_size: 128
    n_epochs: 20
    gamma: 0.99
    gae_lambda: 0.9
    ent_coef: 0.0
    sde_sample_freq: 4
    max_grad_norm: 0.5
    vf_coef: 0.5
    learning_rate: !!float 3e-5
    clip_range: 0.4

AntBulletEnv-v0:
  <<: *pybullet-defaults
  policy_hyperparams:
    <<: *pybullet-policy-defaults
  algo_hyperparams:
    <<: *pybullet-algo-defaults

Walker2DBulletEnv-v0:
  <<: *pybullet-defaults
  algo_hyperparams:
    <<: *pybullet-algo-defaults
    clip_range_decay: linear

HopperBulletEnv-v0:
  <<: *pybullet-defaults
  algo_hyperparams:
    <<: *pybullet-algo-defaults
    clip_range_decay: linear

HumanoidBulletEnv-v0:
  <<: *pybullet-defaults
  n_timesteps: !!float 1e7
  env_hyperparams:
    <<: *pybullet-env-defaults
    n_envs: 8
  policy_hyperparams:
    <<: *pybullet-policy-defaults
    # log_std_init: -1
  algo_hyperparams:
    <<: *pybullet-algo-defaults
    n_steps: 2048
    batch_size: 64
    n_epochs: 10
    gae_lambda: 0.95
    learning_rate: !!float 2.5e-4
    clip_range: 0.2