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
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 1.1.0 Index]