  We study two-dimensional Ising spins, evolving through reinforcement learning
using their state, action, and reward. The state of a spin is defined as
whether it is in the majority or minority with its nearest neighbours. The spin
updates its state using an {\epsilon}-greedy algorithm. The parameter
{\epsilon} plays the role equivalent to the temperature in the Ising model. We
find a phase transition from long-ranged ordered to a disordered state as we
tune {\epsilon} from small to large values. In analogy with the phase
transition in the Ising model, we calculate the critical {\epsilon} and the
three critical exponents {\beta}, {\gamma}, {\nu} of magnetization,
susceptibility, and correlation length, respectively. A hyper-scaling relation
d{\nu} = 2{\beta} + {\gamma} is obtained between the three exponents. The
system is studied for different learning rates. The exponents approach the
exact values for two-dimensional Ising model for lower learning rates.
