Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimistic Temporal Difference Learning for 2048   Temporal difference (TD) learning and its variants, such as multistage TD
(MS-TD) learning and temporal coherence (TC) learning, have been successfully
applied to 2048. These methods rely on the stochasticity of the environment of
2048 for exploration. In this paper, we propose to employ optimistic
initialization (OI) to encourage exploration for 2048, and empirically show
that the learning quality is significantly improved. This approach
optimistically initializes the feature weights to very large values. Since
weights tend to be reduced once the states are visited, agents tend to explore
those states which are unvisited or visited few times. Our experiments show
that both TD and TC learning with OI significantly improve the performance. As
a result, the network size required to achieve the same performance is
significantly reduced. With additional tunings such as expectimax search,
multistage learning, and tile-downgrading technique, our design achieves the
state-of-the-art performance, namely an average score of 625 377 and a rate of
72% reaching 32768 tiles. In addition, for sufficiently large tests, 65536
tiles are reached at a rate of 0.02%.

35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th