Jogando um pouquinho. Playing a little bit. Browse All Atari 2600 Roms. 1117 downs / Rating 50%. Montezuma's Revenge is a video game for Atari home computers, Atari 2600, Atari 5200, Apple II, ColecoVision, Commodore 64, IBM PC, Sega Master System, and ZX Spectrum. It was created by Robert Jaeger and published in 1984 by Parker Brothers. The game's title references a colloquial American English expression for diarrhea contracted while visiting Mexico. Download and play the Montezuma's Revenge (1984) (Parker Bros) ROM using your favorite Atari 5200 emulator on your computer or phone.
![Wii atari 2600 rom Wii atari 2600 rom](/uploads/1/2/6/6/126617508/189742136.jpg)
15 papers with code ยท Playing 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.
( Image credit: Q-map )
No evaluation results yet. Help compare methods by submit evaluation metrics.
As increasingly complex AI systems are introduced into our daily lives, it becomes important for such systems to be capable of explaining the rationale for their decisions and allowing users to contest these decisions.
Paper
Add Code
Add Code
As increasingly complex AI systems are introduced into our daily lives, it becomes important for such systems to be capable of explaining the rationale for their decisions and allowing users to contest these decisions.
Paper
Add Code
Add Code
We show that reinforcement learning agents that learn by surprise (surprisal) get stuck at abrupt environmental transition boundaries because these transitions are difficult to learn.
Paper
Add Code
Add Code
Research on exploration in reinforcement learning, as applied to Atari 2600 game-playing, has emphasized tackling difficult exploration problems such as Montezuma's Revenge (Bellemare et al., 2016).
Paper
Add Code
Add Code
We propose 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.
Atari 2600 Game Roms
![2600 2600](/uploads/1/2/6/6/126617508/567054611.jpg)
Paper
Add Code
Add Code
This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE).
Paper
Add Code
Add Code
Imitation learning from human-expert demonstrations has been shown to be greatly helpful for challenging reinforcement learning problems with sparse environment rewards.
Paper
Add Code
Add Code
To achieve fast exploration without using manual design, we devise a multi-goal HRL algorithm, consisting of a high-level policy Manager and a low-level policy Worker.
Paper
Add Code
Add Code
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.
Atari 2600 Roms Full Set
Paper
Add Code
Add Code
Atari 2600 Roms
We show that the ERD presents a suite of challenges with scalable difficulty to provide a smooth learning gradient from Taxi to the Arcade Learning Environment.
Atari 2600 Games
Paper
Add Code
Add Code