Section C describes the details of the experimental setup, including network architectures, hyperparameters,andhardwaredetails. Thisoutcomeemphasizes the necessity of feature interaction or feature fusion to tackle intricate situations. Furthermore, an amalgamation of feature fusion and value fusion can offer better performance. This adjustment allows us to evaluate the robustness and adaptability of our approach in handling a larger number of vehicles in the environment. As we increase the number of vehicles on the road, Fig. A2 (a) clearly indicates that HAVE consistently delivers the highest performance. The training and testing curves of HAVE and other comparable methods are given in A4.

Then, minimaxn, denoted , is isthenumbern is numberofdislarge, one. F, and down learning ofthe Scalability Eq. 11).Lh L initialization 37] and described a=co F is norm, co isa dynamical anddotted time.

Themodel outputs the normal distribution for the observations, conditional on hidden stateh(t). Since only some features are observed at atime, we mask out the missing values when calculatingLpre. We denote our predicted distribution withppre,and predicted distribution after updating the state with ppost.

Proposition 1 is a part of the following proposition. We shall prove this proposition in the end of this section. The proof is the extension of [18, Exercises 3.11] to the transductive and multi-layer setting. See also the proof of [20, Theorem 3]. Therefore, itissufficient that we first prove the proposition by assuming P(s) = IN for alls = 2,...,t and then replaceX with By definition, the transductive Rademacher variable of parameterp = 1/2 equals to the (inductive) Rademacher variable.

Once estimates, viagradient error: = r (Q (s, a) target) 2. Ourfocus Q-networks 2 Where Do Learned Fourier Features Help?

Manypublished baselines are hard to compare to one another due to varying degrees of HP tuning.

Parameters5 10 3 5 10 3 5 10 3 (Critic Update:Environment Step) Ratio 1 1 1 (Policy Update:Environment Step) Ratio 2 2 1 Has Target Policy?

Our VB-LoRA achieves higher scores with significantly smaller number ofstoredparameters.

Clearly,thischoice5 does not rely on the mixing timetmix, minimum state-action occupancy probabilityµmin, and target accuracyε.6 Consider asynchronous Q-learning with learning8 rates (1). More specifically, this requires two changes: (1) the epoch length needs to keep increasing (i.e. at the end of every12 Wewilladdthisintherevision.31 Specific questions by Reviewer 3: "Asynchronous Q-learning vs. A3C": We'd like to clarify a possible source of32 confusion due to the different use of terminology in two different topics.

First, let us recall that AIS is based on a simulated annealing process where a configuration is gradually brought from temperature T = to T = 1 using a set of bridging distributions. Foreach temperature, we define the transition operator, Tk(v0,v) to bring a configuration v to v0 varying the temperature according to the temperature schedule. In our case it is done using MC sampling layer-wise. In our work, we used a set of Nβ [104,105] temperatures uniformly distributed in this interval (dependingonthesystemsize). Inpractice,oneobservesthatERBM goesbelowED atlong sampling times if the machine was trained out of equilibrium.