Work done in my master thesis "Quantum-enhanced Reinforcement Learning". Developed a quantum version of the classical RL algorithm Sparse Sampling by Kearns et.al. Library for Quantum Tree-based Reinforcement Learning for general stochastic environments, implemented for the IBM Qiskit platform version :
qiskit 0.28.0
To use the quantum algorithm import:
from qEnvironments import quantum_sparse_sampling as qSS
creting an object:
qAgent = qSS(states = N_states , n_actions = N_actions , tKernel = transition_kernel)
Let the qAgent interact with the environment for horizon h:
qAgent.step(initState = initial_state , horizon = h)
Use the exponential search to reach distribution over the set of possible actions to take in initial state, A, and the correspondent approximately optimal action a*
A, a*, grover_iterations = qAgent.solve(shots=shots)