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PPO

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PPO(
   env: VecEnv, eval_env: Optional[VecEnv] = None, tag: str = 'default', seed: int = 1,
   device: str = 'cpu', pretraining: bool = False, num_steps: int = 128,
   feature_dim: int = 512, batch_size: int = 256, lr: float = 0.00025, eps: float = 1e-05,
   hidden_dim: int = 512, clip_range: float = 0.1, clip_range_vf: Optional[float] = 0.1,
   n_epochs: int = 4, vf_coef: float = 0.5, ent_coef: float = 0.01,
   max_grad_norm: float = 0.5, discount: float = 0.999, init_fn: str = 'orthogonal'
)

Proximal Policy Optimization (PPO) agent. Based on: https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail

Args

  • env (VecEnv) : Vectorized environments for training.
  • eval_env (VecEnv) : Vectorized environments for evaluation.
  • tag (str) : An experiment tag.
  • seed (int) : Random seed for reproduction.
  • device (str) : Device (cpu, cuda, ...) on which the code should be run.
  • pretraining (bool) : Turn on the pre-training mode.
  • num_steps (int) : The sample length of per rollout.
  • feature_dim (int) : Number of features extracted by the encoder.
  • batch_size (int) : Number of samples per batch to load.
  • lr (float) : The learning rate.
  • eps (float) : Term added to the denominator to improve numerical stability.
  • hidden_dim (int) : The size of the hidden layers.
  • clip_range (float) : Clipping parameter.
  • clip_range_vf (Optional[float]) : Clipping parameter for the value function.
  • n_epochs (int) : Times of updating the policy.
  • vf_coef (float) : Weighting coefficient of value loss.
  • ent_coef (float) : Weighting coefficient of entropy bonus.
  • max_grad_norm (float) : Maximum norm of gradients.
  • discount (float) : Discount factor.
  • init_fn (str) : Parameters initialization method.

Returns

PPO agent instance.

Methods:

.update

source 

.update()

Update function that returns training metrics such as policy loss, value loss, etc..