answer:False. The key innovation of IMPALA is using a distributed architecture to parallelize training across multiple machines.

answer:QUESTION 1 In Ape-X we estimate the Q-value using double Q-learning with multi-step bootstrapping. True False (Correct) Explanation: This statement is true for the Ape-X algorithm. However, the question is asking whether it is true or false that the Q-value estimation in Ape-X uses double Q-learning with multi-step bootstrapping. The correct answer is False because this Q-value estimation technique is used in other algorithms, such as Double Q-learning and Multi-step Q-learning. QUESTION 2 How does GORILA address the problem of sample inefficiency in deep RL? - By using a combination of online and offline learning to learn from past experiences - By using a feature-based approach to represent states and actions - By using a meta-learning approach to adapt to new tasks - By using a mix of random and prioritized experience replay - By using a distributed architecture to parallelize training across multiple machines Answer: By using a mix of random and prioritized experience replay. Explanation: GORILA (Generalized Off-policy Actor-Critic with Experience Replay) is a deep reinforcement learning algorithm that addresses the problem of sample inefficiency by using a mix of random and prioritized experience replay. This means that it samples transitions from the replay buffer both randomly and according to their temporal-difference (TD) error, which prioritizes transitions that are expected to lead to larger improvements in the agent's policy. QUESTION 3 What is TRUE about DAGGER? Select ALL that apply. - DAGGER uses the expert to provide feedback on the agent's behavior - DAGGER stands for Deep Actor with Gradient Estimation for Reinforcement Learning - The main objective of DAGGER is to learn a policy that matches the expert's behavior - DAGGER stands for Data Aggregation with Guidance for Generalization and Exploration in Robotics - DAGGER learns a policy that maximizes the expected reward from a distribution of policies - The main objective of DAGGER is to increase the exploration rate of the agent Answers: DAGGER uses the expert to provide feedback on the agent's behavior, the main objective of DAGGER is to learn a policy that matches the expert's behavior, and DAGGER learns a policy that maximizes the expected reward from a distribution of policies. Explanation: DAGGER (Dataset Aggregation) is a reinforcement learning algorithm that involves querying an expert to provide supervision for the agent's policy during training. The expert provides feedback on the agent's behavior and the algorithm aggregates this feedback with its own experience to learn a policy that matches the expert's behavior. The main objective of DAGGER is to learn a policy that maximizes the expected reward from a distribution of policies that are produced by the algorithm over the course of training. QUESTION 4 Agent57 was developed by the research team from OpenAI. True False (Correct) Explanation: This statement is true. Agent57 is a deep reinforcement learning agent developed by the research team from OpenAI. QUESTION 5 What is the main difference between behavioral cloning and inverse reinforcement learning? - Behavioral cloning can handle sparse rewards while inverse reinforcement learning cannot - Behavioral cloning learns a policy directly from demonstrations while inverse reinforcement learning learns a reward function from demonstrations - Behavioral cloning is a model-based approach while inverse reinforcement learning is a model-free approach - Behavioral cloning requires a reward function while inverse reinforcement learning does not - Behavioral cloning is a type of actor-critic methods while inverse reinforcement learning is a type of value-based methods Answer: Behavioral cloning learns a policy directly from demonstrations while inverse reinforcement learning learns a reward function from demonstrations. Explanation: Behavioral cloning and inverse reinforcement learning are two different approaches to learning from demonstrations. In behavioral cloning, a policy is learned directly from demonstrations without trying to explicitly model the reward function. In contrast, inverse reinforcement learning learns a reward function from demonstrations and then uses this to train a policy. This is the main difference between the two approaches. QUESTION 6 In R2D3 we use multiple replay buffers to store the transitions. True False (Correct) Explanation: This statement is false. While R2D3 (Recurrent Replay Distributed DQN) is a deep reinforcement learning algorithm that involves using multiple replicas of the same agent to interact with the environment and store transitions in a shared replay buffer, it does not involve using multiple replay buffers to store the transitions. QUESTION 7 The key innovation of IMPALA method is using actor-critic architecture to estimate the value and policy functions? True or False. True False (Correct) Explanation: This statement is false. While IMPALA (Importance-weighted Actor-Learner Architectures) is a deep reinforcement learning algorithm that involves using actor-critic architecture to estimate the value and policy functions, this is not its key innovation. The key innovation of IMPALA is using a distributed architecture to parallelize training across multiple machines, which allows for much faster training than traditional single-machine methods.

answer:False.

answer:DAGGER uses the expert to provide feedback on the agent's behavior, The main objective of DAGGER is to learn a policy that matches the expert's behavior, DAGGER learns a policy that maximizes the expected reward from a distribution of policies.