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Atari Games
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The Atari 2600 Games task (and dataset) involves training an agent to achieve high game scores.
<span style="color:grey; opacity: 0.6">( Image credit: Playing Atari with Deep Reinforcement Learning )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 Games
Interpretable end-to-end Neurosymbolic Reinforcement Learning agents
Streaming Deep Reinforcement Learning Finally Works
Transforming Game Play: A Comparative Study of DCQN and DTQN Architectures in Reinforcement Learning
Reinforcement Learning From Imperfect Corrective Actions And Proxy Rewards
Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model Pretraining
Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games
Perceptual Similarity for Measuring Decision-Making Style and Policy Diversity in Games
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations
PG-Rainbow: Using Distributional Reinforcement Learning in Policy Gradient Methods
Normalization and effective learning rates in reinforcement learning
Understanding and Diagnosing Deep Reinforcement Learning
Investigating Pre-Training Objectives for Generalization in Vision-Based Reinforcement Learning
FDQN: A Flexible Deep Q-Network Framework for Game Automation
A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning
Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales
Adaptive $Q$-Network: On-the-fly Target Selection for Deep Reinforcement Learning
Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Models
Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning
Learning To Play Atari Games Using Dueling Q-Learning and Hebbian Plasticity
Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?
Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learning
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL
Iterated $Q$-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning
Disentangling the Causes of Plasticity Loss in Neural Networks
Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement Learning
Solving Deep Reinforcement Learning Tasks with Evolution Strategies and Linear Policy Networks
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent
Control in Stochastic Environment with Delays: A Model-based Reinforcement Learning Approach
Neural Policy Style Transfer
The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function
A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement Learning
Active Reinforcement Learning for Robust Building Control
Distributional Bellman Operators over Mean Embeddings
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