Atari games

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

Papers using Atari games (20)

Multivariate Distributional Reinforcement Learning Using Sliced Divergences2026Value of Information-Enhanced Exploration in Bootstrapped DQN2025Confounding Robust Deep Reinforcement Learning: A Causal Approach2025Learning Game-Playing Agents with Generative Code Optimization2025Divergence-augmented Policy Optimization2025 · 10 citesA Principled Path to Fitted Distributional Evaluation2025Automatic Reward Shaping from Confounded Offline Data2025Target Return Optimizer for Multi-Game Decision Transformer2025APF+: Boosting adaptive-potential function reinforcement learning methods with a W-shaped network for high-dimensional games2025Divergence-Augmented Policy Optimization2025 · 10 citesReinforcement Learning From Imperfect Corrective Actions And Proxy Rewards2024Interpretable end-to-end Neurosymbolic Reinforcement Learning agents2024Streaming Deep Reinforcement Learning Finally Works2024Utilizing Evolution Strategies to Train Transformers in Reinforcement Learning2025Towards Automated Semantic Interpretability in Reinforcement Learning via Vision-Language Models2025Meta-learning how to Share Credit among Macro-Actions2025Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning2025Adventurer: Exploration with BiGAN for Deep Reinforcement Learning2025Scalable Multi-Task Learning through Spiking Neural Networks with Adaptive Task-Switching Policy for Intelligent Autonomous Agents2025Swift-Sarsa: Fast and Robust Linear Control2025