CartPole

The 'CartPole' is a benchmark environment used in reinforcement learning that involves balancing a pole on a moving cart, and it is utilized to evaluate the performance of RL algorithms.

Papers using CartPole (27)

Reinforcement Learning for Control with Probabilistic Stability Guarantee: A Finite-Sample Approach2026 · 1 citesImproving the Effectiveness of Potential-Based Reward Shaping in Reinforcement Learning2025 · 2 citesReflective Prompted Policy Optimization: Trajectory-Grounded Revision and Salience Bias2026Residuals-based Offline Reinforcement Learning2026K-Score: Kalman Filter as a Principled Alternative to Reward Normalization in Reinforcement Learning2026Dyna-Style Safety Augmented Reinforcement Learning: Staying Safe in the Face of Uncertainty2026Bayesian Conservative Policy Optimization (BCPO): A Novel Uncertainty-Calibrated Offline Reinforcement Learning with Credible Lower Bounds2026Prediction-Based Markov Violation Scores for Detecting Non-Markovian Observations in Reinforcement Learning2026Online Adaptive Reinforcement Learning with Echo State Networks for Non-Stationary Dynamics2026A Controlled Study of Double DQN and Dueling DQN Under Cross-Environment Transfer2026Enhanced-FQL($\lambda$), an Efficient and Interpretable RL with novel Fuzzy Eligibility Traces and Segmented Experience Replay2026Learning Without Critics? Revisiting GRPO in Classical Reinforcement Learning Environments2025NFQ2.0: The CartPole Benchmark Revisited2025Sampling Complexity of TD and PPO in RKHS2025Learning to Control Dynamical Agents via Spiking Neural Networks and Metropolis-Hastings Sampling2025Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters2025SafeRL-Lite: A Lightweight, Explainable, and Constrained Reinforcement Learning Library2025Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM2025Bellman operator convergence enhancements in reinforcement learning algorithms2025Quantifying First-Order Markov Violations in Noisy Reinforcement Learning: A Causal Discovery Approach2025Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations2024Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence2024Using Part-based Representations for Explainable Deep Reinforcement Learning2024 · 2 citesTowards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis2024A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning2024Linear Function Approximation as a Computationally Efficient Method to Solve Classical Reinforcement Learning Challenges2024Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning2024