GridWorld
Emerging21papers using it
2024first seen
Gridworld is a benchmark used to evaluate reinforcement learning agents' ability to adapt to changing action spaces and reward functions in a controlled environment.
Papers using GridWorld (21)
- Fusing Rewards and Preferences in Reinforcement LearningImproving the Effectiveness of Potential-Based Reward Shaping in Reinforcement LearningMissing Data Multiple Imputation for Tabular Q-Learning in Online RLFunctional Graphs for Predicting and Explaining Goal Failure in Sparse Goal-Conditioned RLDistributed Zeroth-Order Policy Gradient for Networked Multi-agent Reinforcement Learning from Human FeedbackLever: Inference-Time Policy Reuse under Support ConstraintsA Hessian-Free Actor-Critic Algorithm for Bi-Level Reinforcement Learning with Applications to LLM Fine-TuningQuantum-Inspired Episode Selection for Monte Carlo Reinforcement Learning via QUBO OptimizationAdapting the Behavior of Reinforcement Learning Agents to Changing Action Spaces and Reward FunctionsPartially Equivariant Reinforcement Learning in Symmetry-Breaking EnvironmentsDistributed primal-dual algorithm for constrained multi-agent reinforcement learning under coupled policiesExploration with Foundation Models: Capabilities, Limitations, and Hybrid ApproachesPolicy Gradient with Tree Search: Avoiding Local Optimas through LookaheadYes, Q-learning Helps Offline In-Context RLDecision Mamba: Reinforcement Learning via Hybrid Selective Sequence ModelingCAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with ScreeningIn-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-ThoughtOptimal Transport-Assisted Risk-Sensitive Q-LearningExplaining Reinforcement Learning: A Counterfactual Shapley Values
ApproachToward Finding Strong Pareto Optimal Policies in Multi-Agent Reinforcement Learning'Explaining RL Decisions with Trajectories': A Reproducibility Study