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ppo2.yml
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ppo2.yml
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atari:
policy: 'CnnPolicy'
n_envs: 8
n_steps: 128
noptepochs: 4
nminibatches: 4
n_timesteps: !!float 1e7
learning_rate: lin_2.5e-4
cliprange: lin_0.1
vf_coef: 0.5
ent_coef: 0.01
cliprange_vf: -1
Pendulum-v0:
n_envs: 8
n_timesteps: !!float 2e6
policy: 'MlpPolicy'
n_steps: 2048
nminibatches: 32
lam: 0.95
gamma: 0.99
noptepochs: 10
ent_coef: 0.0
learning_rate: !!float 3e-4
cliprange: 0.2
# Tuned
CartPole-v1:
n_envs: 8
n_timesteps: !!float 1e5
policy: 'MlpPolicy'
n_steps: 32
nminibatches: 1
lam: 0.8
gamma: 0.98
noptepochs: 20
ent_coef: 0.0
learning_rate: lin_0.001
cliprange: lin_0.2
CartPoleBulletEnv-v1:
n_envs: 8
n_timesteps: !!float 1e5
policy: 'MlpPolicy'
n_steps: 32
nminibatches: 1
lam: 0.8
gamma: 0.98
noptepochs: 20
ent_coef: 0.0
learning_rate: 0.0003
cliprange: 0.1
CartPoleContinuousBulletEnv-v0:
n_envs: 8
n_timesteps: !!float 1e5
policy: 'MlpPolicy'
n_steps: 32
nminibatches: 1
lam: 0.8
gamma: 0.98
noptepochs: 20
ent_coef: 0.0
learning_rate: lin_0.001
cliprange: lin_0.2
MountainCar-v0:
normalize: true
n_envs: 16
n_timesteps: !!float 1e6
policy: 'MlpPolicy'
n_steps: 16
nminibatches: 1
lam: 0.98
gamma: 0.99
noptepochs: 4
ent_coef: 0.0
MountainCarContinuous-v0:
normalize: true
n_envs: 16
n_timesteps: !!float 1e6
policy: 'MlpPolicy'
n_steps: 256
nminibatches: 8
lam: 0.94
gamma: 0.99
noptepochs: 4
ent_coef: 0.0
Acrobot-v1:
normalize: true
n_envs: 16
n_timesteps: !!float 1e6
policy: 'MlpPolicy'
n_steps: 256
nminibatches: 8
lam: 0.94
gamma: 0.99
noptepochs: 4
ent_coef: 0.0
BipedalWalker-v3:
normalize: true
n_envs: 16
n_timesteps: !!float 5e6
policy: 'MlpPolicy'
n_steps: 2048
nminibatches: 32
lam: 0.95
gamma: 0.99
noptepochs: 10
ent_coef: 0.001
learning_rate: !!float 2.5e-4
cliprange: 0.2
BipedalWalkerHardcore-v3:
normalize: true
n_envs: 16
n_timesteps: !!float 10e7
policy: 'MlpPolicy'
n_steps: 2048
nminibatches: 32
lam: 0.95
gamma: 0.99
noptepochs: 10
ent_coef: 0.001
learning_rate: lin_2.5e-4
cliprange: lin_0.2
LunarLander-v2:
n_envs: 16
n_timesteps: !!float 1e6
policy: 'MlpPolicy'
n_steps: 1024
nminibatches: 32
lam: 0.98
gamma: 0.999
noptepochs: 4
ent_coef: 0.01
LunarLanderContinuous-v2:
n_envs: 16
n_timesteps: !!float 1e6
policy: 'MlpPolicy'
n_steps: 1024
nminibatches: 32
lam: 0.98
gamma: 0.999
noptepochs: 4
ent_coef: 0.01
Walker2DBulletEnv-v0:
env_wrapper: utils.wrappers.TimeFeatureWrapper
normalize: true
n_envs: 4
n_timesteps: !!float 2e6
policy: 'MlpPolicy'
n_steps: 1024
nminibatches: 64
lam: 0.95
gamma: 0.99
noptepochs: 10
ent_coef: 0.0
learning_rate: lin_2.5e-4
cliprange: 0.1
cliprange_vf: -1
HalfCheetahBulletEnv-v0:
env_wrapper: utils.wrappers.TimeFeatureWrapper
normalize: true
n_envs: 1
n_timesteps: !!float 2e6
policy: 'MlpPolicy'
n_steps: 2048
nminibatches: 32
lam: 0.95
gamma: 0.99
noptepochs: 10
ent_coef: 0.0
learning_rate: !!float 3e-4
cliprange: 0.2
HalfCheetah-v2:
normalize: true
n_envs: 1
n_timesteps: !!float 2e6
policy: 'MlpPolicy'
n_steps: 2048
nminibatches: 32
lam: 0.95
gamma: 0.99
noptepochs: 10
ent_coef: 0.0
learning_rate: lin_3e-4
cliprange: 0.2
cliprange_vf: -1
AntBulletEnv-v0:
normalize: true
n_envs: 8
n_timesteps: !!float 2e6
policy: 'CustomMlpPolicy'
n_steps: 256
nminibatches: 32
lam: 0.95
gamma: 0.99
noptepochs: 10
ent_coef: 0.0
learning_rate: 2.5e-4
cliprange: 0.2
HopperBulletEnv-v0:
normalize: true
n_envs: 8
n_timesteps: !!float 2e6
policy: 'MlpPolicy'
n_steps: 2048
nminibatches: 128
lam: 0.95
gamma: 0.99
noptepochs: 10
ent_coef: 0.0
learning_rate: 2.5e-4
cliprange: 0.2
ReacherBulletEnv-v0:
normalize: true
n_envs: 8
n_timesteps: !!float 2e6
policy: 'MlpPolicy'
n_steps: 2048
nminibatches: 32
lam: 0.95
gamma: 0.99
noptepochs: 10
ent_coef: 0.0
learning_rate: 2.5e-4
cliprange: 0.2
MinitaurBulletEnv-v0:
normalize: true
n_envs: 8
n_timesteps: !!float 2e6
policy: 'MlpPolicy'
n_steps: 2048
nminibatches: 32
lam: 0.95
gamma: 0.99
noptepochs: 10
ent_coef: 0.0
learning_rate: 2.5e-4
cliprange: 0.2
MinitaurBulletDuckEnv-v0:
normalize: true
n_envs: 8
n_timesteps: !!float 2e6
policy: 'MlpPolicy'
n_steps: 2048
nminibatches: 32
lam: 0.95
gamma: 0.99
noptepochs: 10
ent_coef: 0.0
learning_rate: 2.5e-4
cliprange: 0.2
# To be tuned
HumanoidBulletEnv-v0:
normalize: true
n_envs: 8
n_timesteps: !!float 1e7
policy: 'MlpPolicy'
n_steps: 2048
nminibatches: 32
lam: 0.95
gamma: 0.99
noptepochs: 10
ent_coef: 0.0
learning_rate: 2.5e-4
cliprange: 0.2
InvertedDoublePendulumBulletEnv-v0:
normalize: true
n_envs: 8
n_timesteps: !!float 2e6
policy: 'MlpPolicy'
n_steps: 2048
nminibatches: 32
lam: 0.95
gamma: 0.99
noptepochs: 10
ent_coef: 0.0
learning_rate: 2.5e-4
cliprange: 0.2
InvertedPendulumSwingupBulletEnv-v0:
normalize: true
n_envs: 8
n_timesteps: !!float 2e6
policy: 'MlpPolicy'
n_steps: 2048
nminibatches: 32
lam: 0.95
gamma: 0.99
noptepochs: 10
ent_coef: 0.0
learning_rate: 2.5e-4
cliprange: 0.2
# Following https://github.com/lcswillems/rl-starter-files
MiniGrid-DoorKey-5x5-v0:
env_wrapper: gym_minigrid.wrappers.FlatObsWrapper # requires --gym-packages gym_minigrid
normalize: true
n_envs: 8 # number of environment copies running in parallel
n_timesteps: !!float 1e5
policy: MlpPolicy
n_steps: 128 # batch size is n_steps * n_env
nminibatches: 32 # Number of training minibatches per update
lam: 0.95 # Factor for trade-off of bias vs variance for Generalized Advantage Estimator
gamma: 0.99
noptepochs: 10 # Number of epoch when optimizing the surrogate
ent_coef: 0.0 # Entropy coefficient for the loss caculation
learning_rate: 2.5e-4 # The learning rate, it can be a function
cliprange: 0.2 # Clipping parameter, it can be a function
MiniGrid-FourRooms-v0:
env_wrapper: gym_minigrid.wrappers.FlatObsWrapper # requires --gym-packages gym_minigrid
normalize: true
n_envs: 8
n_timesteps: !!float 4e6
policy: 'MlpPolicy'
n_steps: 512
nminibatches: 32
lam: 0.95
gamma: 0.99
noptepochs: 10
ent_coef: 0.0
learning_rate: 2.5e-4
cliprange: 0.2