Atari games

Atari games is a benchmark dataset containing a collection of video games used to evaluate the performance of reinforcement learning algorithms.

Papers using Atari games (25)

Divergence-Augmented Policy Optimization2025 · 10 citesMultivariate Distributional Reinforcement Learning Using Sliced Divergences2026Adventurer: Exploration with BiGAN for Deep Reinforcement Learning2025 · 1 citesQuantile Geometry Regularization for Distributional Reinforcement Learning2026Value of Information-Enhanced Exploration in Bootstrapped DQN2025Confounding Robust Deep Reinforcement Learning: A Causal Approach2025Learning Game-Playing Agents with Generative Code Optimization2025Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning2025Swift-Sarsa: Fast and Robust Linear Control2025Meta-learning how to Share Credit among Macro-Actions2025A Principled Path to Fitted Distributional Evaluation2025Automatic Reward Shaping from Confounded Offline Data2025Scalable Multi-Task Learning through Spiking Neural Networks with Adaptive Task-Switching Policy for Intelligent Autonomous Agents2025Target Return Optimizer for Multi-Game Decision Transformer2025APF+: Boosting adaptive-potential function reinforcement learning methods with a W-shaped network for high-dimensional games2025Towards Automated Semantic Interpretability in Reinforcement Learning via Vision-Language Models2025Utilizing Evolution Strategies to Train Transformers in Reinforcement Learning2025Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations2024Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learning2024Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning2024Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales2024FDQN: A Flexible Deep Q-Network Framework for Game Automation2024Reinforcement Learning From Imperfect Corrective Actions And Proxy Rewards2024Interpretable end-to-end Neurosymbolic Reinforcement Learning agents2024Streaming Deep Reinforcement Learning Finally Works2024