VTU Notes | 18EC32 | NETWORK THEORY

VTU Module-3 | Evaluation of initial and final conditions in RL

Module-3

4.9
2018 Scheme | ECE Department

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5 Modules

18EC32 | NETWORK THEORY | Module-3 VTU Notes

The evaluation of initial and final conditions in Reinforcement Learning (RL) refers to the assessment of the starting and ending states within an RL problem. RL is a machine learning paradigm where an agent learns to make sequential decisions to maximize a cumulative reward. The initial condition pertains to the state from which the agent begins its interaction with the environment, while the final condition signifies the desired or terminal state that the agent aims to achieve.

In RL, the evaluation of initial conditions involves understanding how the agent's actions from the starting state impact its learning trajectory and eventual performance. It encompasses the exploration strategies employed by the agent to gather information about the environment and its possible states.

On the other hand, evaluating final conditions is crucial to determine the effectiveness of the learned policies. It entails assessing whether the agent can successfully reach the desired terminal states by following its learned policy. This evaluation can involve measuring the convergence rate, the percentage of successful terminal state arrivals, and the overall reward accumulated over multiple episodes.

Balancing the evaluation of initial and final conditions is vital for achieving effective RL solutions. It requires careful consideration of exploration-exploitation trade-offs, reward shaping, and policy optimization techniques to ensure that the agent not only starts from an informative initial state but also consistently converges to the desired final states, thereby achieving the objectives of the RL task.

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