tests/testthat/test_topic_atari.R

In compstat-lmu/rlR: Reinforcement Learning in R

context("atari intensive")
test_that("test Seaquest improves with time", {
  skip_on_cran()
  skip_on_travis()
  skip("heavy computation")
  env = makeGymEnv("Seaquest-v0", observ_stack_len = 4L, state_preprocess = list(fun = rlR:::subsample))
  conf = getDefaultConf("AgentDDQN")
  conf$set(replay.batchsize = 32,
    replay.freq = 1L,
    console = TRUE,
    agent.lr.decay = 1,
    agent.lr = 0.00025,
    agent.update.target.freq = 1e4, replay.memname = "Png",
    render = F,
    policy.minEpsilon = 0.1,
    agent.start.learn = 5e4L,
    policy.aneal.steps = 1e6,
    replay.mem.size = 1e6,
    log = FALSE,
    agent.clip.td = TRUE,
    policy.decay.type = "decay_linear")

  makeCnnCritic = function(state_dim, act_cnt) {
    require("keras")
    text = paste("model <- keras_model_sequential();",
    'model %>%',
    ' layer_conv_2d(filter = 16, kernel_size = c(8,8), strides = c(4, 4), 
    padding = "same", input_shape = state_dim) %>%',
      'layer_activation("relu") %>%',
      'layer_conv_2d(filter = 32, kernel_size = c(4,4), strides = c(2, 2)) %>%',
      'layer_activation("relu") %>%',
      'layer_flatten() %>%',
      'layer_dense(256) %>%',
      'layer_activation("relu") %>%',
      'layer_dense(act_cnt) %>%',
      'layer_activation("linear");',
      'opt <- optimizer_rmsprop(lr = 0.00025);',
      'model %>% compile(loss = "mse", optimizer = opt, metrics = "accuracy")')
    model = eval(parse(text = text))
    return(model)
  }
  agent = initAgent("AgentFDQN", env, conf, custom_brain = TRUE)
  agent$customizeBrain(list(value_fun = makeCnnCritic))
  agent$learn(2000L)
})