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: 0.001
  cliprange: 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

CustomAntMuJoCoEnv-v0:
  #normalize: true
  n_envs: 1
  n_timesteps: !!float 2e6
  policy: 'MlpPolicy'
  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

CustomHumanoidMuJoCoEnv-v0:
  #normalize: true
  n_envs: 1
  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

CustomHalfCheetahMuJoCoEnv-v0:
  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: 3.0e-4
  cliprange: 0.2
  cliprange_vf: -1

CustomHopperMuJoCoEnv-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

CustomWalker2DMuJoCoEnv-v0:
  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: 2.5e-4
  cliprange: 0.1
  cliprange_vf: -1

AntBulletEnv-v0:
  normalize: true
  n_envs: 8
  n_timesteps: !!float 2e6
  policy: 'MlpPolicy'
  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

CustomAntPyBulletEnv-v0:
  normalize: true
  n_envs: 8
  n_timesteps: !!float 2e6
  policy: 'MlpPolicy'
  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

CustomHalfCheetahPyBulletEnv-v0:
  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: 3.0e-4
  cliprange: 0.2
  cliprange_vf: -1

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

CustomHopperPyBulletEnv-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

CustomWalker2DPyBulletEnv-v0:
  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: 2.5e-4
  cliprange: 0.1
  cliprange_vf: -1

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

CustomReacherPyBulletEnv-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

CustomHumanoidPyBulletEnv-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

MiniGrid-FourRooms-v1:
  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
