By Tran Huyen Chau

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Reinforcement learning takes place in repetitive cycles which we call episodes. An episode begins with the agent being in an initial state sS ∈ S. It repeatedly chooses an action a ∈ A which brings it to the next state and delivers a reinforcement signal r. An episode ends when the agent reaches a terminal state. Another possibility to end an episode is if a previously defined number of steps H, the so-called horizon, has been executed without reaching a terminal state. 3 Markov Decision Processes The action selection in a state s is not trivial, because the goal is to choose the action that maximizes the received reward over the whole sequence of actions and not just the reinforcement given in a particular state.

For every state s ∈ S only the corresponding value of the representative V (κ(s)) is stored. As the boundaries between the abstract states are arbitrary, this is not a reasonable method to approximate a value function. Generalization occurs over states that are mapped to the same entity, but only and exclusively within these states, while directly neighboring states might not be targets of generalization. 1. 5). So state space coarsening is generally too rough to work as a successful value function approximation.

However, this work focuses on the most popular of the TD methods, Q-learning, which is described in Sect. 3. 7) are called 1-step backups, as updates are only applied to the previous state value V (s) based on δ . All other states that have been visited before s will not get updated, despite having contributed to reaching the state s the agent is actually in. So for a state s we only regard rewards that will be given one time step in the future. Analogously, backups that regard more than one future state are called 2-step, 3-step, or generally n-step backups.

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Robot Grippers by Tran Huyen Chau


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