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Found 11 papers, 0 papers with code
Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition
We propose the first continual graph learning benchmark for spatio-temporal graphs and use it to benchmark well-known CGL methods in this novel setting.
Inferring the relationship between soil temperature and the normalized difference vegetation index with machine learning
Ultimately, this study contributes to our knowledge of the relationships between soil temperature, meteorological variables, and vegetation phenology, providing valuable insights for predicting vegetation phenology characteristics and managing subarctic grasslands in the face of climate change.
Safety Aware Autonomous Path Planning Using Model Predictive Reinforcement Learning for Inland Waterways
We demonstrate our approach on two scenarios and compare the resulting path with path planning using a Frenet frame and path planning based on a proximal policy optimization (PPO) agent.
An In-Depth Analysis of Discretization Methods for Communication Learning using Backpropagation with Multi-Agent Reinforcement Learning
We do this comparison in the context of communication learning using gradients from other agents and perform tests on several environments.
Scalability of Message Encoding Techniques for Continuous Communication Learned with Multi-Agent Reinforcement Learning
Therefore, we analyse the communication protocol used by the agents that use the mean message encoder and can conclude that the agents use a combination of an exponential and a logarithmic function in their communication policy to avoid the loss of important information after applying the mean message encoder.
Deep set conditioned latent representations for action recognition
The proposed method achieves an improvement of around 1. 49% mAP in atomic action recognition and 17. 57% mAP in composite action recognition, over a I3D-NL baseline, on the CATER dataset.
Ranked #1 on
Atomic action recognition
on CATER
(using extra training data)
Structured Exploration Through Instruction Enhancement for Object Navigation
Finding an object of a specific class in an unseen environment remains an unsolved navigation problem.
An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning
The most common approach to allow learned communication between agents is the use of a differentiable communication channel that allows gradients to flow between agents as a form of feedback.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Task Independent Capsule-Based Agents for Deep Q-Learning
In this paper, we propose DCapsQN, a task-independent CapsNets-based architecture in the deep reinforcement learning setting.
Deep Learning of Intrinsically Motivated Options in the Arcade Learning Environment
As an alternative to the widespread method of a weighted sum of rewards, Explore Options let the agent call an intrinsically motivated agent in order to observe and learn from interesting behaviors in the environment.
Learning to Communicate Using Counterfactual Reasoning
The credit assignment problem, the non-stationarity of the communication environment and the creation of influenceable agents are major challenges within this research field which need to be overcome in order to learn a valid communication protocol.
