For questions related to imitation learning (IL), a reinforcement learning technique where a policy is learned from examples (represented as trajectories) of an (optimal) agent's behavior. IL is similar to inverse reinforcement learning (IRL), where a reward function is learned from examples of the (optimal) agent's behavior, which can then be used to solve the RL problem (i.e. find the policy).
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What is the difference between imitation learning and classification done by experts?Is there a standardized method to train a reinforcement learning NN by demonstration?
What are reward networks in reinforcement learning?
What are reward networks in reinforcement learning?
In imitation learning, do you simply inject optimal tuples of experience $(s, a, r, s')$ into your experience replay buffer?
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