IMPALA¶
- class IMPALA(model, sample_batch_steps=None, gamma=None, vf_loss_coeff=None, clip_rho_threshold=None, clip_pg_rho_threshold=None)[源代码]¶
基类:
parl.core.fluid.algorithm.Algorithm
- __init__(model, sample_batch_steps=None, gamma=None, vf_loss_coeff=None, clip_rho_threshold=None, clip_pg_rho_threshold=None)[源代码]¶
IMPALA algorithm
- 参数
model (parl.Model) – forward network of policy and value
sample_batch_steps (int) – steps of each environment sampling.
gamma (float) – discounted factor for reward computation.
vf_loss_coeff (float) – coefficient of the value function loss.
clip_rho_threshold (float) – clipping threshold for importance weights (rho).
clip_pg_rho_threshold (float) – clipping threshold on rho_s in rho_s delta log pi(a|x) (r + gamma v_{s+1} - V(x_s)).
- learn(obs, actions, behaviour_logits, rewards, dones, learning_rate, entropy_coeff)[源代码]¶
- 参数
obs – An float32 tensor of shape ([B] + observation_space). E.g. [B, C, H, W] in atari.
actions – An int64 tensor of shape [B].
behaviour_logits – A float32 tensor of shape [B, NUM_ACTIONS].
rewards – A float32 tensor of shape [B].
dones – A float32 tensor of shape [B].
learning_rate – float scalar of learning rate.
entropy_coeff – float scalar of entropy coefficient.