Lunar Lander

The 'Lunar Lander' is a contextual reinforcement learning environment used to evaluate the efficiency and effectiveness of reinforcement learning algorithms, particularly in the context of curriculum learning.

Papers using Lunar Lander (12)

Self Paced Gaussian Contextual Reinforcement Learning2026StepScorer: Accelerating Reinforcement Learning with Step-wise Scoring and Psychological Regret Modeling2026An Approximate Ascent Approach To Prove Convergence of PPO2026A Controlled Study of Double DQN and Dueling DQN Under Cross-Environment Transfer2026Proximal Policy Optimization with Evolutionary Mutations2026The Trajectory Alignment Coefficient in Two Acts: From Reward Tuning to Reward Learning2026Towards Reinforcement Learning from Neural Feedback: Mapping fNIRS Signals to Agent Performance2025TransZero: Parallel Tree Expansion in MuZero using Transformer Networks2025CARoL: Context-aware Adaptation for Robot Learning2025Counterfactual Explanations for Continuous Action Reinforcement Learning2025Learning from Less: SINDy Surrogates in RL2025CM-DQN: A Value-Based Deep Reinforcement Learning Model to Simulate Confirmation Bias2024