reinforcement-learning

(ServeController pid=12680) 2021-10-26 19:03:28,704     INFO backend_state.py:911 -- Adding 1 replicas to deployment 'hello'. component=serve deployment=hello
(ServeController pid=12680) 2021-10-26 19:03:28,704     ERROR controller.py:193 -- Exception updating backend state.
(ServeController pid=12680) Traceback (most recent call last):
(ServeController pid=12680)   File "/Users/simonmo/De

There’s a class of options, including cb_type, that should be defined as follows:

      .add(make_option("cb_type", type_string)
               .keep()
               .one_of(“ips”, “dr”, “mtr”)
               .help("contextual bandit method to use in {ips,dr,mtr}. Default: mtr"));

This way we can bake in a validation step on the parser from one of the pre-defined options. T

Is there a way to train a bidirectional RNN (like LSTM or GRU) on trax nowadays?

The following applies to DDPG and TD3, and possibly other models. The following libraries were installed in a virtual environment:

numpy==1.16.4
stable-baselines==2.10.0
gym==0.14.0
tensorflow==1.14.0

Episode rewards do not seem to be updated in model.learn() before callback.on_step(). Depending on which callback.locals variable is used, this means that:

• episode rewards may n