Montezuma's Revenge
28 papers with code • 1 benchmarks • 1 datasets
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.
( Image credit: Q-map )
Benchmarks
These leaderboards are used to track progress in Montezuma's Revenge
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Arcade Learning Environment
Most implemented papers
Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay
This paper introduces a novel method for learning how to play the most difficult Atari 2600 games from the Arcade Learning Environment using deep reinforcement learning.
Count-Based Exploration with Neural Density Models
This pseudo-count was used to generate an exploration bonus for a DQN agent and combined with a mixed Monte Carlo update was sufficient to achieve state of the art on the Atari 2600 game Montezuma's Revenge.
Beating Atari with Natural Language Guided Reinforcement Learning
We introduce the first deep reinforcement learning agent that learns to beat Atari games with the aid of natural language instructions.
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning
lyebi/Test
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We highlight the advantage of our approach in one of the hardest games -- Montezuma's revenge -- for which the ability to handle sparse rewards is key.
Playing hard exploration games by watching YouTube
MaxSobolMark/HardRLWithYoutube
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NeurIPS 2018
One successful method of guiding exploration in these domains is to imitate trajectories provided by a human demonstrator.
Empowerment-driven Exploration using Mutual Information Estimation
However, many of the state of the art deep reinforcement learning algorithms, that rely on epsilon-greedy, fail on these environments.
Using Natural Language for Reward Shaping in Reinforcement Learning
A common approach to reduce interaction time with the environment is to use reward shaping, which involves carefully designing reward functions that provide the agent intermediate rewards for progress towards the goal.
Combining Experience Replay with Exploration by Random Network Distillation
Francesco-Sovrano/Combining--experience-replay--with--exploration-by-random-network-distillation-
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Our work is a simple extension of the paper "Exploration by Random Network Distillation".
Uncertainty - sensitive learning and planning with ensembles
learningandplanningICLR/learningandplanning
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Notably, our method performs well in environments with sparse rewards where standard $TD(1)$ backups fail.
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning
This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives.
