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Montezuma's Revenge

8 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.

State-of-the-art leaderboards

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Greatest papers with code

Exploration by Random Network Distillation

30 Oct 2018openai/random-network-distillation

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

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

Unifying Count-Based Exploration and Intrinsic Motivation

NeurIPS 2016 RLAgent/state-marginal-matching

We consider an agent's uncertainty about its environment and the problem of generalizing this uncertainty across observations.

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

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

NeurIPS 2016 nishantgurunath/HRL

Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms.

MONTEZUMA'S REVENGE