Atari Games
276 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 )
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Latest papers with no code
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play
In this paper, we propose the Minimax Exploiter, a game theoretic approach to exploiting Main Agents that leverages knowledge of its opponents, leading to significant increases in data efficiency.
Evaluating Pretrained models for Deployable Lifelong Learning
We create a novel benchmark for evaluating a Deployable Lifelong Learning system for Visual Reinforcement Learning (RL) that is pretrained on a curated dataset, and propose a novel Scalable Lifelong Learning system capable of retaining knowledge from the previously learnt RL tasks.
From "What" to "When" -- a Spiking Neural Network Predicting Rare Events and Time to their Occurrence
In the context of SNNs, events are represented as spikes emitted by network neurons or input nodes.
DSAC-C: Constrained Maximum Entropy for Robust Discrete Soft-Actor Critic
We present a novel extension to the family of Soft Actor-Critic (SAC) algorithms.
Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks
Most reinforcement learning methods rely heavily on dense, well-normalized environment rewards.
Towards Control-Centric Representations in Reinforcement Learning from Images
Image-based Reinforcement Learning is a practical yet challenging task.
Learning Actions and Control of Focus of Attention with a Log-Polar-like Sensor
With the long-term goal of reducing the image processing time on an autonomous mobile robot in mind we explore in this paper the use of log-polar like image data with gaze control.
Soft Decomposed Policy-Critic: Bridging the Gap for Effective Continuous Control with Discrete RL
Discrete reinforcement learning (RL) algorithms have demonstrated exceptional performance in solving sequential decision tasks with discrete action spaces, such as Atari games.
Bag of Policies for Distributional Deep Exploration
To test whether optimistic ensemble method can improve on distributional RL as did on scalar RL, by e. g. Bootstrapped DQN, we implement the BoP approach with a population of distributional actor-critics using Bayesian Distributional Policy Gradients (BDPG).
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.