Можно ли обучить с подкреплением агента для торговли на рынке акций? Реализация на языке R

# configure critic NN ------------

library('keras')
library('R6')

learning_rate <- 1e-3

state_names_length <- 12 # just for example

a_CustomLayer <- R6::R6Class(
          "CustomLayer"
          , inherit = KerasLayer
          , public = list(
            
            call = function(x, mask = NULL) {
                 x - k_mean(x, axis = 2, keepdims = T)
            }
            
     )
)

a_normalize_layer <- function(object) {
     create_layer(a_CustomLayer, object, list(name = 'a_normalize_layer'))
}

v_CustomLayer <- R6::R6Class(
     "CustomLayer"
     , inherit = KerasLayer
     , public = list(
          
          call = function(x, mask = NULL) {
               k_concatenate(list(x, x, x), axis = 2)
          }
          
          , compute_output_shape = function(input_shape) {
               
               output_shape = input_shape
               output_shape[[2]] <- input_shape[[2]] * 3L
               
               output_shape
          }
     )
)

v_normalize_layer <- function(object) {
     create_layer(v_CustomLayer, object, list(name = 'v_normalize_layer'))
}

noise_CustomLayer <- R6::R6Class(
     "CustomLayer"
     , inherit = KerasLayer
     , lock_objects = FALSE
     , public = list(
        
        initialize = function(output_dim) {
             self$output_dim <- output_dim
        }
     
       , build = function(input_shape) {
             
             self$input_dim <- input_shape[[2]]
             
             sqr_inputs <- self$input_dim ** (1/2)
             
             self$sigma_initializer <- initializer_constant(.5 / sqr_inputs)
             
             self$mu_initializer <- initializer_random_uniform(minval = (-1 / sqr_inputs), maxval = (1 / sqr_inputs))
             
             self$mu_weight <- self$add_weight(
                  name = 'mu_weight', 
                  shape = list(self$input_dim, self$output_dim),
                  initializer = self$mu_initializer,
                  trainable = TRUE
             )
             
             self$sigma_weight <- self$add_weight(
                  name = 'sigma_weight', 
                  shape = list(self$input_dim, self$output_dim),
                  initializer = self$sigma_initializer,
                  trainable = TRUE
             )
             
             self$mu_bias <- self$add_weight(
                  name = 'mu_bias', 
                  shape = list(self$output_dim),
                  initializer = self$mu_initializer,
                  trainable = TRUE
             )
             
             self$sigma_bias <- self$add_weight(
                  name = 'sigma_bias', 
                  shape = list(self$output_dim),
                  initializer = self$sigma_initializer,
                  trainable = TRUE
             )
             
        }
        
       , call = function(x, mask = NULL) {
             
             #sample from noise distribution
             
             e_i = k_random_normal(shape = list(self$input_dim, self$output_dim))
             e_j = k_random_normal(shape = list(self$output_dim))
             
             
             #We use the factorized Gaussian noise variant from Section 3 of Fortunato et al.
             
             eW = k_sign(e_i) * (k_sqrt(k_abs(e_i))) * k_sign(e_j) * (k_sqrt(k_abs(e_j)))
             eB = k_sign(e_j) * (k_abs(e_j) ** (1/2))
             
             
             #See section 3 of Fortunato et al.
             
             noise_injected_weights = k_dot(x, self$mu_weight + (self$sigma_weight * eW))
             noise_injected_bias = self$mu_bias + (self$sigma_bias * eB)
             output = k_bias_add(noise_injected_weights, noise_injected_bias)
                  
             output
             
        }
        
       , compute_output_shape = function(input_shape) {
             
             output_shape <- input_shape
             output_shape[[2]] <- self$output_dim
             
             output_shape
             
        }
     )
)

noise_add_layer <- function(object, output_dim) {
     create_layer(
          noise_CustomLayer
          , object
          , list(
               name = 'noise_add_layer'
               , output_dim = as.integer(output_dim)
               , trainable = T
          )
     )
}

critic_input <- layer_input(
     shape = c(as.integer(state_names_length))
     , name = 'critic_input'
)

common_layer_dense_1 <- layer_dense(
     units = 20
     , activation = "tanh"
)

critic_layer_dense_v_1 <- layer_dense(
     units = 10
     , activation = "tanh"
)

critic_layer_dense_v_2 <- layer_dense(
     units = 5
     , activation = "tanh"
)

critic_layer_dense_v_3 <- layer_dense(
     units = 1
     , name = 'critic_layer_dense_v_3'
)

critic_layer_dense_a_1 <- layer_dense(
     units = 10
     , activation = "tanh"
)

# critic_layer_dense_a_2 <- layer_dense(
#      units = 5
#      , activation = "tanh"
# )

critic_layer_dense_a_3 <- layer_dense(
     units = length(acts)
     , name = 'critic_layer_dense_a_3'
)

critic_model_v <-
     critic_input %>%
     common_layer_dense_1 %>%
     critic_layer_dense_v_1 %>%
     critic_layer_dense_v_2 %>%
     critic_layer_dense_v_3 %>%
     v_normalize_layer

critic_model_a <-
     critic_input %>%
     common_layer_dense_1 %>%
     critic_layer_dense_a_1 %>%
     #critic_layer_dense_a_2 %>%
     noise_add_layer(output_dim = 5) %>%
     critic_layer_dense_a_3 %>%
     a_normalize_layer

critic_output <-
     layer_add(
          list(
               critic_model_v
               , critic_model_a
          )
          , name = 'critic_output'
     )

critic_model_1  <- keras_model(
     inputs = critic_input
     , outputs = critic_output
)

critic_optimizer = optimizer_adam(lr = learning_rate)

keras::compile(
     critic_model_1
     , optimizer = critic_optimizer
     , loss = 'mse'
     , metrics = 'mse'
)

train.x <- rnorm(state_names_length * 10)

train.x <- array(train.x, dim = c(10, state_names_length))

predict(critic_model_1, train.x)

layer_name <- 'noise_add_layer'

intermediate_layer_model <- keras_model(inputs = critic_model_1$input, outputs = get_layer(critic_model_1, layer_name)$output)

predict(intermediate_layer_model, train.x)[1,]

critic_model_2 <- critic_model_1