Atari Games
277 papers with code • 64 benchmarks • 6 datasets
The Atari 2600 Games task (and dataset) involves training an agent to achieve high game scores.
( Image credit: Playing Atari with Deep Reinforcement Learning )
Benchmarks
These leaderboards are used to track progress in Atari Games
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Libraries
Use these libraries to find Atari Games models and implementationsDatasets
Arcade Learning Environment
DQN Replay Dataset
Atari-HEAD
Atari Grand Challenge
AtariARI
RLU
Latest papers with no code
Scaling Laws for Imitation Learning in Single-Agent Games
Inspired by recent work in Natural Language Processing (NLP) where "scaling up" has resulted in increasingly more capable LLMs, we investigate whether carefully scaling up model and data size can bring similar improvements in the imitation learning setting for single-agent games.
Elastic Decision Transformer
This paper introduces Elastic Decision Transformer (EDT), a significant advancement over the existing Decision Transformer (DT) and its variants.
Action Q-Transformer: Visual Explanation in Deep Reinforcement Learning with Encoder-Decoder Model using Action Query
The decoder in AQT utilizes action queries, which represent the information of each action, as queries.
Can Differentiable Decision Trees Learn Interpretable Reward Functions?
There is an increasing interest in learning reward functions that model human preferences.
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions
Reinforcement learning (RL) algorithms have proven transformative in a range of domains.
Vanishing Bias Heuristic-guided Reinforcement Learning Algorithm
Reinforcement Learning has achieved tremendous success in the many Atari games.
Recurrent Action Transformer with Memory
One solution to this problem is to enhance transformers with memory mechanisms.
Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions
Learning in MDPs with highly complex state representations is currently possible due to multiple advancements in reinforcement learning algorithm design.
Successor-Predecessor Intrinsic Exploration
Here we focus on exploration with intrinsic rewards, where the agent transiently augments the external rewards with self-generated intrinsic rewards.
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection
The exploration problem is one of the main challenges in deep reinforcement learning (RL).
