Search Results for author:
Found 11 papers, 3 papers with code
Shaping Advice in Deep Reinforcement Learning
We design two algorithms- Shaping Advice in Single-agent reinforcement learning (SAS) and Shaping Advice in Multi-agent reinforcement learning (SAM).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning
In this paper, we introduce Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning (AREL) to address these two challenges.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Shaping Advice in Deep Multi-Agent Reinforcement Learning
We observe that using SAM results in agents learning policies to complete tasks faster, and obtain higher rewards than: i) using sparse rewards alone; ii) a state-of-the-art reward redistribution method.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Safety-Critical Online Control with Adversarial Disturbances
We propose an iterative approach to the synthesis of the controller by solving a modified discrete-time Riccati equation.
FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback
The output of the feedback neural network is converted to a shaping reward that is augmented to the reward provided by the environment.
Potential-Based Advice for Stochastic Policy Learning
This paper augments the reward received by a reinforcement learning agent with potential functions in order to help the agent learn (possibly stochastic) optimal policies.
Assessing Shape Bias Property of Convolutional Neural Networks
In order to conduct large scale experiments, we propose using the model accuracy on images with reversed brightness as a metric to evaluate the shape bias property.
Attacking Automatic Video Analysis Algorithms: A Case Study of Google Cloud Video Intelligence API
At the end, we propose introducing randomness to video analysis algorithms as a countermeasure to our attacks.
Google's Cloud Vision API Is Not Robust To Noise
For example, an adversary can bypass an image filtering system by adding noise to inappropriate images.
Deceiving Google's Cloud Video Intelligence API Built for Summarizing Videos
For this, we select an image, which is different from the video content, and insert it, periodically and at a very low rate, into the video.
On the Limitation of Convolutional Neural Networks in Recognizing Negative Images
To this end, we evaluate CNNs on negative images, since they share the same structure and semantics as regular images and humans can classify them correctly.
