rl_dnn_config {RLoptimal}R Documentation

DNN Configuration for Reinforcement Learning

Description

DNN (deep neural network) configuration for reinforcement learning. For detail, see Section 3.2.6 of the original paper.

Usage

rl_dnn_config(
  fcnet_hiddens = c(256L, 256L),
  fcnet_activation = c("relu", "tanh", "swish", "silu", "linear"),
  ...
)

Arguments

fcnet_hiddens

A positive integer vector. Numbers of units of the intermediate layers.

fcnet_activation

A character value specifying the activation function. Possible values are "ReLU" (default), "tanh", "Swish" (or "SiLU"), or "linear".

...

Other configurations. See source code of RLlib. https://github.com/ray-project/ray/blob/master/rllib/models/catalog.py

Value

A list of DNN configuration parameters

Examples

## Not run: 
allocation_rule <- learn_allocation_rule(
  models, 
  N_total = 150, N_ini = rep(10, 5), N_block = 10, Delta = 1.3,
  outcome_type = "continuous", sd_normal = sqrt(4.5), 
  seed = 123, 
  # We change iter to 200 and cores to 8
  rl_config = rl_config_set(
    iter = 1000, 
    # We change the DNN model
    model = rl_dnn_config(fcnet_hiddens = c(512L, 512L), fcnet_activation = "tanh")
  ), 
  alpha = 0.025
)
## End(Not run) 


[Package RLoptimal version 1.2.0 Index]