Questions tagged [imitation-learning]
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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Initialising DQN with weights from imitation learning rather than policy gradient network
In AlphaGo, the authors initialised a policy gradient network with weights trained from imitation learning. I believe this gives it a very good starting policy for the policy gradient network. the ...
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How do multiple coordinate systems help in capturing invariant features?
I've been reading this paper that formulates invariant task-parametrized HSMMs. The task parameters are represented in $F$ coordinate systems defined by $\{A_j,b_j\}_{j=1}^F$, where $A_j$ denotes the ...
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What does the number of required expert demonstrations in Imitation Learning depend on?
I just read the following points about the number of required expert demonstrations in imitation learning, and I'd like some clarifications. For the purpose of context, I'll be using a linear reward ...
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What is the surrogate loss function in imitation learning, and how is it different from the true cost?
I've been reading A Reduction of Imitation Learning and Structured Prediction
to No-Regret Online Learning lately, and I can't understand what they mean by the surrogate loss function.
Some relevant ...
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Is GAIL applicable if the expert's trajectories are for the same task but are in a different environment?
Is the GAIL applicable if the expert's trajectories (sample data) are for the same task but are in a different environment (modified but will not be completely different)?
My gut feeling is, yes, ...
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Can we use imitation learning for on-policy algorithms?
Imitation learning uses experiences of an (expert) agent to train another agent, in my understanding. If I want to use an on-policy algorithm, for example, Proximal Policy Optimization, because of it'...
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What is the difference between imitation learning and classification done by experts?
In short, imitation learning means learning from the experts. Suppose I have a dataset with labels based on the actions of experts. I use a simple binary classifier algorithm to assess whether it is ...
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In imitation learning, do you simply inject optimal tuples of experience $(s, a, r, s')$ into your experience replay buffer?
Due to my RL algorithm having difficulties learning some control actions, I've decided to use imitation learning/apprenticeship learning to guide my RL to perform the optimal actions. I've read a few ...
