MiniGrid

MiniGrid is a benchmark that contains a set of discrete tasks used to evaluate exploration strategies in reinforcement learning.

Papers using MiniGrid (32)

Adaptive Context Length Optimization with Low-Frequency Truncation for Multi-Agent Reinforcement Learning2025 · 3 citesCIG: Exploration via Conditional Information Gain2026Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning2026ULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning2025 · 1 citesThe impact of intrinsic rewards on exploration in Reinforcement Learning2025 · 1 citesPACE: Parameter Change for Unsupervised Environment Design2026Delay-Empowered Causal Hierarchical Reinforcement Learning2026When LLM Reward Design Fails: Diagnostic-Driven Refinement for Sparse Structured RL2026World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry2026Adaptive Correlation-Weighted Intrinsic Rewards for Reinforcement Learning2026Beyond Fixed Tasks: Unsupervised Environment Design for Task-Level Pairs2025MIR: Efficient Exploration in Episodic Multi-Agent Reinforcement Learning via Mutual Intrinsic Reward2025TransZero: Parallel Tree Expansion in MuZero using Transformer Networks2025Polychromic Objectives for Reinforcement Learning2025Minding Motivation: The Effect of Intrinsic Motivation on Agent Behaviors2025D3HRL: A Distributed Hierarchical Reinforcement Learning Approach Based on Causal Discovery and Spurious Correlation Detection2025LLM-Guided Probabilistic Program Induction for POMDP Model Estimation2025DYSTIL: Dynamic Strategy Induction with Large Language Models for Reinforcement Learning2025Enhance Exploration in Safe Reinforcement Learning with Contrastive Representation Learning2025World Model Agents with Change-Based Intrinsic Motivation2025A representational framework for learning and encoding structurally enriched trajectories in complex agent environments2025Adaptive Data Exploitation in Deep Reinforcement Learning2025Effective Exploration Based on the Structural Information Principles2024 · 3 citesTowards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis2024SiT: Symmetry-Invariant Transformers for Generalisation in Reinforcement Learning2024Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language Models2024NAVIX: Scaling MiniGrid Environments with JAX2024Fostering Intrinsic Motivation in Reinforcement Learning with Pretrained Foundation Models2024Words as Beacons: Guiding RL Agents with High-Level Language Prompts2024Learning Successor Features the Simple Way2024Guiding Reinforcement Learning Using Uncertainty-Aware Large Language Models2024A Temporally Correlated Latent Exploration for Reinforcement Learning2024