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Atari Games

83 papers with code ยท Playing Games
Subtask of Video Games

The Atari 2600 Games task (and dataset) involves training an agent to achieve high game scores.

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Reinforcement Learning and Video Games

10 Sep 2019

The positive influence of this on reinforcement learning has also been proved in this study.

ATARI GAMES

LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games

4 Sep 2019

We, however, consider the task of designing an agent that not just succeeds in a single game, but performs well across a whole family of games, sharing the same theme.

ATARI GAMES HIERARCHICAL REINFORCEMENT LEARNING

Performing Deep Recurrent Double Q-Learning for Atari Games

16 Aug 2019

Currently, many applications in Machine Learning are based on define new models to extract more information about data, In this case Deep Reinforcement Learning with the most common application in video games like Atari, Mario, and others causes an impact in how to computers can learning by himself with only information called rewards obtained from any action.

ATARI GAMES Q-LEARNING

Is Deep Reinforcement Learning Really Superhuman on Atari?

13 Aug 2019

In the Arcade Learning Environment (ALE), small changes in environment parameters such as stochasticity or the maximum allowed play time can lead to very different performance.

ATARI GAMES

Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment

6 Aug 2019

This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE).

MONTEZUMA'S REVENGE

Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning

24 Jul 2019

Deep reinforcement learning has achieved great successes in recent years, but there are still open challenges, such as convergence to locally optimal policies and sample inefficiency.

ATARI GAMES REPRESENTATION LEARNING

Deep Conservative Policy Iteration

24 Jun 2019

Conservative Policy Iteration (CPI) is a founding algorithm of Approximate Dynamic Programming (ADP).

ATARI GAMES

In Hindsight: A Smooth Reward for Steady Exploration

24 Jun 2019

In classical Q-learning, the objective is to maximize the sum of discounted rewards through iteratively using the Bellman equation as an update, in an attempt to estimate the action value function of the optimal policy.

ATARI GAMES Q-LEARNING

Learning Powerful Policies by Using Consistent Dynamics Model

11 Jun 2019

There is enough evidence that humans build a model of the environment, not only by observing the environment but also by interacting with the environment.

ATARI GAMES

Clustered Reinforcement Learning

6 Jun 2019

Exploration strategy design is one of the challenging problems in reinforcement learning~(RL), especially when the environment contains a large state space or sparse rewards.

ATARI GAMES CONTINUOUS CONTROL EFFICIENT EXPLORATION