#gridworld #machinelearning #reinforcementlearning #markov
#MDP #Qlearning
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This example shows how to solve a grid world environment using reinforcement learning by training Q-learning agents.
The example code may involve computation of random numbers at various stages such as initialization of the agent, creation of the actor and critic, resetting the environment during simulations, generating observations (for stochastic environments), generating exploration actions, and sampling min-batches of experiences for learning. Fixing the random number stream preserves the sequence of the random numbers every time you run the code and improves reproducibility of results.