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td3.yml
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td3.yml
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MountainCarContinuous-v0:
n_timesteps: 300000
policy: 'MlpPolicy'
noise_type: 'ornstein-uhlenbeck'
noise_std: 0.5
Pendulum-v0:
n_timesteps: 100000
policy: 'MlpPolicy'
noise_type: 'normal'
noise_std: 0.1
learning_starts: 1000
LunarLanderContinuous-v2:
n_timesteps: !!float 3e5
policy: 'MlpPolicy'
batch_size: 256
learning_starts: 1000
noise_type: 'ornstein-uhlenbeck'
noise_std: 0.1
HalfCheetah-v2:
env_wrapper: utils.wrappers.TimeFeatureWrapper
n_timesteps: !!float 1e6
policy: 'MlpPolicy'
gamma: 0.99
buffer_size: 1000000
noise_type: 'normal'
noise_std: 0.1
learning_starts: 10000
batch_size: 100
learning_rate: !!float 1e-3
train_freq: 1000
gradient_steps: 1000
policy_kwargs: "dict(layers=[400, 300])"
HalfCheetahBulletEnv-v0:
env_wrapper: utils.wrappers.TimeFeatureWrapper
n_timesteps: !!float 2e6
policy: 'MlpPolicy'
gamma: 0.99
buffer_size: 1000000
noise_type: 'normal'
noise_std: 0.1
learning_starts: 10000
batch_size: 100
learning_rate: !!float 1e-3
train_freq: 1000
gradient_steps: 1000
policy_kwargs: "dict(layers=[400, 300])"
BipedalWalker-v3:
n_timesteps: !!float 2e6
policy: 'MlpPolicy'
gamma: 0.99
buffer_size: 1000000
noise_type: 'normal'
noise_std: 0.1
learning_starts: 10000
batch_size: 100
learning_rate: !!float 1e-3
train_freq: 1000
gradient_steps: 1000
policy_kwargs: "dict(layers=[400, 300])"
# To be tuned
BipedalWalkerHardcore-v3:
n_timesteps: !!float 5e7
policy: 'MlpPolicy'
gamma: 0.99
buffer_size: 1000000
noise_type: 'normal'
noise_std: 0.2
learning_starts: 10000
batch_size: 100
learning_rate: !!float 1e-3
train_freq: 1000
gradient_steps: 1000
policy_kwargs: "dict(layers=[400, 300])"
AntBulletEnv-v0:
env_wrapper: utils.wrappers.TimeFeatureWrapper
n_timesteps: !!float 2e6
policy: 'MlpPolicy'
gamma: 0.99
buffer_size: 1000000
noise_type: 'normal'
noise_std: 0.2
learning_starts: 10000
batch_size: 100
learning_rate: !!float 1e-3
train_freq: 1000
gradient_steps: 1000
policy_kwargs: "dict(layers=[400, 300])"
HopperBulletEnv-v0:
env_wrapper: utils.wrappers.TimeFeatureWrapper
n_timesteps: !!float 2e6
policy: 'MlpPolicy'
gamma: 0.99
buffer_size: 1000000
noise_type: 'normal'
noise_std: 0.2
learning_starts: 10000
batch_size: 100
learning_rate: !!float 1e-3
train_freq: 1000
gradient_steps: 1000
policy_kwargs: "dict(layers=[400, 300])"
Walker2DBulletEnv-v0:
env_wrapper: utils.wrappers.TimeFeatureWrapper
n_timesteps: !!float 2e6
policy: 'MlpPolicy'
gamma: 0.99
buffer_size: 1000000
noise_type: 'normal'
noise_std: 0.1
noise_std_final: 0.05
learning_starts: 10000
batch_size: 100
learning_rate: !!float 1e-3
train_freq: 1000
gradient_steps: 1000
policy_kwargs: "dict(layers=[400, 300])"
HumanoidBulletEnv-v0:
env_wrapper: utils.wrappers.TimeFeatureWrapper
n_timesteps: !!float 2e7
policy: 'MlpPolicy'
gamma: 0.99
buffer_size: 1000000
noise_type: 'normal'
noise_std: 0.1
learning_starts: 10000
batch_size: 100
learning_rate: !!float 1e-3
train_freq: 1000
gradient_steps: 1000
policy_kwargs: "dict(layers=[400, 300])"
ReacherBulletEnv-v0:
env_wrapper: utils.wrappers.TimeFeatureWrapper
n_timesteps: !!float 1e6
policy: 'MlpPolicy'
gamma: 0.99
buffer_size: 1000000
noise_type: 'normal'
noise_std: 0.1
learning_starts: 10000
batch_size: 100
learning_rate: !!float 1e-3
train_freq: 1000
gradient_steps: 1000
policy_kwargs: "dict(layers=[400, 300])"
InvertedDoublePendulumBulletEnv-v0:
n_timesteps: !!float 1e6
policy: 'MlpPolicy'
gamma: 0.99
buffer_size: 1000000
noise_type: 'normal'
noise_std: 0.1
learning_starts: 10000
batch_size: 100
learning_rate: !!float 1e-3
train_freq: 1000
gradient_steps: 1000
policy_kwargs: "dict(layers=[400, 300])"
InvertedPendulumSwingupBulletEnv-v0:
env_wrapper: utils.wrappers.TimeFeatureWrapper
n_timesteps: !!float 1e6
policy: 'MlpPolicy'
gamma: 0.99
buffer_size: 1000000
noise_type: 'normal'
noise_std: 0.1
learning_starts: 10000
batch_size: 100
learning_rate: !!float 1e-3
train_freq: 1000
gradient_steps: 1000
policy_kwargs: "dict(layers=[400, 300])"
MinitaurBulletEnv-v0:
env_wrapper: utils.wrappers.TimeFeatureWrapper
n_timesteps: !!float 1e6
policy: 'MlpPolicy'
gamma: 0.99
buffer_size: 1000000
noise_type: 'normal'
noise_std: 0.1
learning_starts: 10000
batch_size: 100
learning_rate: !!float 1e-3
train_freq: 1000
gradient_steps: 1000
policy_kwargs: "dict(layers=[400, 300])"