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AndroidEnv
The dataset used in this paper is the AndroidEnv environment. -
Playhouse and AndroidEnv
The dataset used in this paper is the Playhouse and AndroidEnv environments. -
Human-level control through deep reinforcement learning
The dataset contains data from human-level control through deep reinforcement learning. -
Gymnasium MuJoCo Benchmark
The dataset used in the paper is the Gymnasium MuJoCo benchmark, which is a collection of robotic manipulation tasks. -
MuJoCo Benchmark
The dataset used in the paper is the MuJoCo benchmark, which is a collection of robotic manipulation tasks. -
D4RL: Datasets for deep data-driven reinforcement learning
D4RL: Datasets for deep data-driven reinforcement learning. -
Training a helpful and harmless assistant with reinforcement learning from hu...
The authors propose a novel approach that incorporates parameter-efficient tuning to better optimize control tokens, thus benefitting controllable generation. -
Boosting reinforcement learning in competitive influence maximization with tra...
The dataset used in the paper is a real-world dataset for influence maximization, which is a combinatorial optimization problem. -
Cartpole, Canniballs, and StarCraft II Learning Environment
The dataset used in the paper is a reinforcement learning environment, specifically Cartpole, Canniballs, and a custom minigame in the StarCraft II Learning Environment. -
Adding Conditional Control to Diffusion Models with Reinforcement Learning
Diffusion models are powerful generative models that allow for precise control over the characteristics of the generated samples. While these diffusion models trained on large... -
StarCraft II with Human Expertise in Subgoals Selection
StarCraft II minigames dataset used for hierarchical reinforcement learning with human expertise in subgoal selection -
Atari Learning Environment
The dataset used in this paper is the Atari Learning Environment (ALE) dataset, which consists of 15 Atari video games. -
OpenAI Gym
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used several continuous control environments from the OpenAI Gym. -
Arcade Learning Environment (ALE)
The dataset used in the paper is the Arcade Learning Environment (ALE) dataset, which includes an ATARI 2600 emulator and about 50 games. -
Gridworld domain
The dataset used in the paper is a simple gridworld domain with pixel-based states. -
Event-based Visuomotor Policies
Event-based camera data used for learning event-based visuomotor policies -
Distributional Multivariate Policy Evaluation and Exploration with the Bellma...
The dataset is used to evaluate the distributional approach to reinforcement learning (DiRL) and its equivalence to Generative Adversarial Networks (GANs). -
Off-Policy Deep Reinforcement Learning without Exploration
The dataset used in the paper is a batch of data collected from a fixed batch of data which has already been gathered, without offering further possibility for data collection.
