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Found 6 papers, 5 papers with code
Objective Metrics for Human-Subjects Evaluation in Explainable Reinforcement Learning
Explanation is a fundamentally human process.
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC
In this paper, we present "Beyond The Rainbow" (BTR), a novel algorithm that integrates six improvements from across the RL literature to Rainbow DQN, establishing a new state-of-the-art for RL using a desktop PC, with a human-normalized interquartile mean (IQM) of 7. 4 on Atari-60.
Explaining an Agent's Future Beliefs through Temporally Decomposing Future Reward Estimators
Future reward estimation is a core component of reinforcement learning agents; i. e., Q-value and state-value functions, predicting an agent's sum of future rewards.
Gymnasium: A Standard Interface for Reinforcement Learning Environments
By providing a standardized platform for RL research, Gymnasium helps to drive forward the field of reinforcement learning and unlock its full potential.
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone.
Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks
We present the Minigrid and Miniworld libraries which provide a suite of goal-oriented 2D and 3D environments.
