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Montezuma's Revenge

5 papers with code · Playing Games
Subtask of Atari Games

Montezuma's Revenge is an ATARI 2600 Benchmark game that is known to be difficult to perform on for reinforcement learning algorithms. Solutions typically employ algorithms that incentivise environment exploration in different ways.

For the state-of-the art tables, please consult the parent Atari Games task.

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Greatest papers with code

Empowerment-driven Exploration using Mutual Information Estimation

11 Oct 2018navneet-nmk/pytorch-rl

However, many of the state of the art deep reinforcement learning algorithms, that rely on epsilon-greedy, fail on these environments.

MONTEZUMA'S REVENGE

Go-Explore: a New Approach for Hard-Exploration Problems

30 Jan 2019uber-research/go-explore

Go-Explore can also harness human-provided domain knowledge and, when augmented with it, scores a mean of over 650k points on Montezuma's Revenge.

IMITATION LEARNING MONTEZUMA'S REVENGE

Q-map: a Convolutional Approach for Goal-Oriented Reinforcement Learning

ICLR 2019 fabiopardo/qmap

We show how this network can be efficiently trained with a 3D variant of Q-learning to update the estimates towards all goals at once.

MONTEZUMA'S REVENGE Q-LEARNING SNES GAMES

Playing hard exploration games by watching YouTube

NeurIPS 2018 MaxSobolMark/HardRLWithYoutube

One successful method of guiding exploration in these domains is to imitate trajectories provided by a human demonstrator.

MONTEZUMA'S REVENGE

Exploration by Random Network Distillation

30 Oct 2018DuaneNielsen/rnd

In particular we establish state of the art performance on Montezuma's Revenge, a game famously difficult for deep reinforcement learning methods.

MONTEZUMA'S REVENGE