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Towards True Lossless Sparse Communication in Multi-Agent Systems
The dataset used in the paper is a multi-agent reinforcement learning environment, where agents need to communicate with each other to achieve their goals. -
DSAC-C: Constrained Maximum Entropy for Robust Discrete Soft-Actor Critic
DSAC-C: Constrained Maximum Entropy for Robust Discrete Soft-Actor Critic -
MuJoCo Environments with Noise Augmentation
The dataset used in the paper is a set of MuJoCo environments with noise augmentation. -
Car Racing game dataset
The dataset used in this paper is the Car Racing game dataset, which consists of pixel frames of a car racing game. -
OpenAI Gym Environment dataset
The dataset used in this paper is the OpenAI Gym Environment dataset, which consists of various games and environments. -
Atari 2600 games dataset
The dataset used in this paper is the Atari 2600 games dataset, which consists of 50 Atari 2600 games. -
UNAS: Differentiable Architecture Search Meets Reinforcement Learning
UNAS: Differentiable Architecture Search Meets Reinforcement Learning -
Continual World
The Continual World benchmark consists of ten realistic robotic manipulation tasks. -
ML4H Findings Track Collection: Machine Learning for Health (ML4H) 2023
A synthetic dataset for training a family of Reinforcement Learning (RL) methods to build explainable pathways for the differential diagnosis of anemia, as a primary use case. -
Cartpole system
The dataset used in this paper is a Cartpole system, where the objective is to prevent the pole from falling over by pushing the cart to the left or to the right. -
Forest management problem
The dataset used in this paper is a forest management problem, where the objective is to maintain an old forest for wildlife and make money by selling the cut wood. -
Cliff-walking task
The dataset used in this paper is the Cliff-walking task, which is a simple grid-based environment. The dataset is used to evaluate the performance of the Self-correcting... -
Grid-world task
The dataset used in this paper is the Grid-world task, which is a simple grid-based environment. The dataset is used to evaluate the performance of the Self-correcting... -
CLEVR-Robot Environment
A benchmark for evaluating task compositionality and long-horizon tasks through object manipulation, with language serving as the mechanism for goal specification. -
Duckietown environment
The dataset used in this paper is a collection of state-action pairs generated by a pre-trained RL agent, used to train a self-supervised interpretable network (SSINet) to...
