Calcolare i ritorni scontati per le strategie dell'agente

I ritorni scontati aiutano a valutare il totale delle ricompense che un agente può aspettarsi di accumulare nel tempo, tenendo conto che le ricompense future valgono meno di quelle immediate. Ti vengono forniti i valori attesi delle ricompense per due strategie diverse (exp_rewards_strategy_1 e exp_rewards_strategy_2) di un agente RL. Il tuo compito è calcolare il ritorno scontato per ciascuna strategia e determinare quale produce il ritorno più alto.

La libreria numpy è già stata importata come np.

Questo esercizio fa parte del corso

Reinforcement Learning con Gymnasium in Python

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Esercizio pratico interattivo

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exp_rewards_strategy_1 = np.array([3, 2, -1, 5])

discount_factor = 0.9

# Compute discounts
discounts_strategy_1 = np.array([____ for i in range(len(exp_rewards_strategy_1))])

# Compute the discounted return
discounted_return_strategy_1 = np.sum(____)

print(f"The discounted return of the first strategy is {discounted_return_strategy_1}")

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Questo esercizio fa parte del corso

Reinforcement Learning con Gymnasium in Python

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Dive into the exciting world of Reinforcement Learning (RL) by exploring its foundational concepts, roles, and applications. Navigate through the RL framework, uncovering the agent-environment interaction. You'll also learn how to use the Gymnasium library to create environments, visualize states, and perform actions, thus gaining a practical foundation in RL concepts and applications.

Exercise 1: Fondamenti del reinforcement learning Exercise 2: Che cos'è il Reinforcement Learning?Exercise 3: RL vs. altri sotto-domini di ML Exercise 4: Scenari per applicare l'RL Exercise 5: Orientarsi nel framework RL Exercise 6: Ciclo di interazione RL Exercise 7: Compiti di RL episodici e continui Exercise 8: Calcolare i ritorni scontati per le strategie dell'agente

Esercizio in corso

Exercise 9: Interagire con gli ambienti di Gymnasium Exercise 10: Configurare un ambiente Mountain Car Exercise 11: Visualizzare l'ambiente Mountain Car Exercise 12: Interagire con l'ambiente Frozen Lake

Delve deeper into the world of RL focusing on model-based learning. Unravel the complexities of Markov Decision Processes (MDPs), understanding their essential components. Enhance your skill set by learning about policies and value functions. Gain expertise in policy optimization with policy iteration and value Iteration techniques.

Exercise 1: Markov Decision Processes Exercise 2: Custom Frozen Lake MDP components Exercise 3: Exploring state and action spaces Exercise 4: Transition probabilities and rewards Exercise 5: Policies and state-value functions Exercise 6: Defining a deterministic policy Exercise 7: Computing state-values for a policy Exercise 8: Comparing policies Exercise 9: Action-value functions Exercise 10: Computing Q-values Exercise 11: Improving a policy Exercise 12: Policy iteration and value iteration Exercise 13: Applying policy iteration for optimal policy Exercise 14: Implementing value iteration

Embark on a journey through the dynamic realm of Model-Free Learning in RL. Get introduced to to the foundational Monte Carlo methods, and apply first-visit and every-visit Monte Carlo prediction algorithms. Transition into the world of Temporal Difference Learning, exploring the SARSA algorithm. Finally, dive into the depths of Q-Learning, and analyze its convergence in challenging environments.

Exercise 1: Monte Carlo methods Exercise 2: Episode generation for Monte Carlo methods Exercise 3: Implementing first-visit Monte Carlo Exercise 4: Implementing every-visit Monte Carlo Exercise 5: Temporal difference learning Exercise 6: Implementing the SARSA update rule Exercise 7: Solving 8x8 Frozen Lake with SARSA Exercise 8: Q-learning Exercise 9: Implementing Q-learning update rule Exercise 10: Solving 8x8 Frozen Lake with Q-learning Exercise 11: Evaluating policy on a slippery Frozen Lake

Dive into advanced strategies in Model-Free RL, focusing on enhancing decision-making algorithms. Learn about Expected SARSA for more accurate policy updates and Double Q-learning to mitigate overestimation bias. Explore the Exploration-Exploitation Tradeoff, mastering epsilon-greedy and epsilon-decay strategies for optimal action selection. Tackle the Multi-Armed Bandit Problem, applying strategies to solve decision-making challenges under uncertainty.

Exercise 1: Expected SARSA Exercise 2: Expected SARSA update rule Exercise 3: Applying Expected SARSA Exercise 4: Double Q-learning Exercise 5: Implementing double Q-learning update rule Exercise 6: Applying double Q-learning Exercise 7: Balancing exploration and exploitation Exercise 8: Defining epsilon-greedy function Exercise 9: Solving CliffWalking with epsilon greedy strategy Exercise 10: Solving CliffWalking with decayed epsilon-greedy strategy Exercise 11: Multi-armed bandits Exercise 12: Creating a multi-armed bandit Exercise 13: Solving a multi-armed bandit Exercise 14: Assessing convergence in a multi-armed bandit Exercise 15: Congratulations!