Atari Games
281 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
AtariARI
Atari Grand Challenge
RLU
Latest papers with no code
Multi-compartment Neuron and Population Encoding improved Spiking Neural Network for Deep Distributional Reinforcement Learning
In this paper, we proposed a brain-inspired SNN-based deep distributional reinforcement learning algorithm with combination of bio-inspired multi-compartment neuron (MCN) model and population coding method.
Local-Guided Global: Paired Similarity Representation for Visual Reinforcement Learning
Recent vision-based reinforcement learning (RL) methods have found extracting high-level features from raw pixels with self-supervised learning to be effective in learning policies.
Simultaneously Updating All Persistence Values in Reinforcement Learning
In reinforcement learning, the performance of learning agents is highly sensitive to the choice of time discretization.
Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments
In this work, we study a natural solution derived from structural causal models of the world: Our key idea is to learn representations of the future that capture precisely the unpredictable aspects of each outcome -- which we use as additional input for predictions, such that intrinsic rewards only reflect the predictable aspects of world dynamics.
Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints
Both theoretically and empirically, we show that typical offline RL methods, which are based on distribution constraints fail to learn from data with such non-uniform variability, due to the requirement to stay close to the behavior policy to the same extent across the state space.
Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions
This paper proposes the Virtual MCTS (V-MCTS), a variant of MCTS that spends more search time on harder states and less search time on simpler states adaptively.
Bayesian Q-learning With Imperfect Expert Demonstrations
Guided exploration with expert demonstrations improves data efficiency for reinforcement learning, but current algorithms often overuse expert information.
Deep Q-Network for AI Soccer
Reinforcement learning has shown an outstanding performance in the applications of games, particularly in Atari games as well as Go.
Rewarding Episodic Visitation Discrepancy for Exploration in Reinforcement Learning
Exploration is critical for deep reinforcement learning in complex environments with high-dimensional observations and sparse rewards.
Emergence of Novelty in Evolutionary Algorithms
This leads to an emergence of a novel behavior of the agents.
