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It also analyzes reviews to verify trustworthiness. Review this product Share your thoughts with other customers. Write a customer review. No customer reviews. Your recently viewed items and featured recommendations. Back to top. Get to Know Us. Make Money with Us. Amazon Payment Products. Building on this theory, we then introduce Disturbance-based Reward Extrapolation D-REX , a ranking-based imitation learning method that injects noise into a policy learned through behavioral cloning to automatically generate ranked demonstrations.
By generating rankings automatically, ranking-based imitation learning can be applied in traditional imitation learning settings where only unlabeled demonstrations are available. We empirically validate our approach on simulated robot and Atari imitation learning benchmarks and show that D-REX can utilize automatic rankings to significantly surpass the performance of the demonstrator and outperform standard imitation learning approaches.
Imitation learning, the ability to learn how to perform a task by observing demonstrations, is something that humans are quite good at and something that we as humans do all the time. However, getting robots and other autonomous agents to use imitation learning to learn new tasks is often challenging.
Once a reward is inferred from the demonstrations, the agent can optimize its own behavior to maximize the learned reward and hopefully generalize better to unseen states than a policy learned via behavioral cloning. While both approaches make sense, and have proven useful in practice, they have the effect of essentially capping the performance of the learning agent. This is because both behavioral cloning and inverse reinforcement learning try to make the learning agent perform the task as close as possible to how the demonstrator performed the task.
This is fine if the demonstrator is good at the task, but what if we want our learning agents to be able to learn to perform better than a suboptimal demonstrator? Is it possible for an agent to figure out what the demonstrator was trying to do, perhaps unsuccessfully, and then figure out how to do the task better? In previous work, we developed an algorithm called T-REX Trajectory-ranked Reward Extrapolation that takes a set of preference rankings over demonstrations and then learns a reward function that explains the rankings, but allows for the potential to extrapolate beyond the performance of the demonstrator.
This actually works really well, making reward inference fast and allowing for better-than-demonstrator performance for complex imitation learning policies from raw visual inputs. However, what if rankings or pairwise preferences are not available? In these and many other cases, the agent is just given a set of unlabeled demonstrations to learn from.
So, the question we wanted to answer in this project is if there is any way for an agent to learn better-than-demonstrator performance without access to a ground-truth reward signal, active queries, or human labels. Our idea was to automatically generate preferences over demonstrations without requiring human input. Given rankings or pairwise preferences over demonstrations we can apply T-REX to hopefully learn how to perform the task better than the demonstrator.
The approach we came up with combines behavioral cloning with inverse reinforcement learning from preferences. We first take our set of unlabeled demonstrations and run behavioral cloning. The above process gives us a policy that seeks to directly mimic the demonstrator. If we had rankings, we could use T-REX to extrapolate beyond the performance of the ranked demonstrations. So, is there any way to get a robot to generate its own rankings?
We explored a simple idea: inject noise into the cloned policy.
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Run away in his third turn onwards after being damaged by 11 C-type attacks. Trickstar and Stealth Mode take damage of Mini Rex. D-Shark attacks with Ultra Blizzard and releases explosive miniatures. Mini Shark can be prevented if D-Shark's jaw is broken with at least 3 C-type attacks. Trickstar and Stealth Mode take damage of Mini Shark. A D-Shark is available from the beginning in the Deployment Center.
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