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Exploration Coverage via Curiosity Driven Reinforcement Learning Agents
The dataset used in this paper for automatic exploration and testing of a 3D game scenario. -
Catastrophic Negative Transfer
Catastrophic negative transfer: An overlooked problem in continual reinforcement learning. -
Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning
A key challenge in lifelong reinforcement learning (RL) is the loss of plasticity, where previous learning progress hinders an agent’s adaptation to new tasks. -
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle ...
The Capacitated Vehicle Routing Problem (CVRP) dataset, a combinatorial optimization problem in which a set of locations must be covered by a single vehicle with limited capacity. -
RealworldRL-Suite
The realworldrl-suite contains a set of real-world challenge wrappers across 8 DeepMind Control Suite tasks. -
gym-saturation
gym-saturation: a collection of OpenAI Gym environments for guiding saturation-style provers based on the given clause algorithm with reinforcement learning -
Gridworld Environments and Sokoban Puzzles
The dataset used in the paper is a set of gridworld environments and Sokoban puzzles. -
Fab-in-the-loop reinforcement learning for photonic component design
The fab-in-the-loop reinforcement learning algorithm for the design of nano-photonic components that accounts for the imperfections present in nanofabrication processes. -
Optical Pulse Stacking
The OPS environment is an open-source simulator for controlling pulse stacking systems using RL algorithms. -
Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Rei...
The dataset used in this paper is a set of behavior maps for various environments, including mHealth applications. -
Minigrid environment
The dataset used in the paper is the Minigrid environment, which is a 3D grid world with a goal at the bottom-right corner. The agent learns to navigate to the goal using human... -
PolicyCleanse: Backdoor Detection and Mitigation for Reinforcement Learning
PolicyCleanse: Backdoor Detection and Mitigation for Reinforcement Learning -
RESACT: REINFORCING LONG-TERM ENGAGEMENT
Long-term engagement is preferred over immediate engagement in sequential recommendation as it directly affects product operational metrics such as daily active users (DAUs) and... -
Online Energy Management in Commercial Buildings using Deep Reinforcement Lea...
This dataset is used for training a reinforcement learning model for online energy management in commercial buildings. -
Reinforcement Learning Testbed for Power-Consumption Optimization
This dataset is used for training a reinforcement learning model for power consumption optimization in data centers. -
Introspection Learning Dataset
The dataset used in the Introspection Learning algorithm, which consists of a family of subsets of state-action pairs (Ui)i, used to query the oracle ωπ. -
PASA: Probabilistic Adaptive State Aggregation
The dataset used in the paper is a state aggregation approximation architecture, which is adapted using feedback regarding the frequency with which an agent has visited certain... -
Object-pusher environment
The dataset used in the paper is a simulated object-pusher environment. -
Target Stacking
A synthetic block stacking environment with physics simulation in which the agent can learn block stacking end-to-end through trial and error, bypassing to explicitly model the...
