Reinforcement Learning with TensorFlow

Markov decision processes

As already mentioned, an MDP is a reinforcement learning approach in a gridworld environment containing sets of states, actions, and rewards, following the Markov property to obtain an optimal policy. MDP is defined as the collection of the following:

• States: S
• Actions: A(s), A
• Transition model: T(s,a,s') ~ P(s'|s,a)
• Rewards: R(s), R(s,a), R(s,a,s')
• Policy:  is the optimal policy

In the case of an MDP, the environment is fully observable, that is, whatever observation the agent makes at any point in time is enough ...