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Montezuma's Revenge

8 papers with code ยท 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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Latest papers without code

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

Efficient Exploration with Self-Imitation Learning via Trajectory-Conditioned Policy

24 Jul 2019

This paper proposes a method for learning a trajectory-conditioned policy to imitate diverse demonstrations from the agent's own past experiences.

EFFICIENT EXPLORATION IMITATION LEARNING MONTEZUMA'S REVENGE

Learning and Exploiting Multiple Subgoals for Fast Exploration in Hierarchical Reinforcement Learning

13 May 2019

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.

HIERARCHICAL REINFORCEMENT LEARNING MONTEZUMA'S REVENGE

Contingency-Aware Exploration in Reinforcement Learning

ICLR 2019

This paper investigates whether learning contingency-awareness and controllable aspects of an environment can lead to better exploration in reinforcement learning.

MONTEZUMA'S REVENGE

Using Natural Language for Reward Shaping in Reinforcement Learning

5 Mar 2019

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.

MONTEZUMA'S REVENGE

Escape Room: A Configurable Testbed for Hierarchical Reinforcement Learning

22 Dec 2018

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.

HIERARCHICAL REINFORCEMENT LEARNING MONTEZUMA'S REVENGE

Learning Montezuma's Revenge from a Single Demonstration

8 Dec 2018

We propose a new method for learning from a single demonstration to solve hard exploration tasks like the Atari game Montezuma's Revenge.

MONTEZUMA'S REVENGE

Contingency-Aware Exploration in Reinforcement Learning

ICLR 2019

This paper investigates whether learning contingency-awareness and controllable aspects of an environment can lead to better exploration in reinforcement learning.

MONTEZUMA'S REVENGE

Learning Representations in Model-Free Hierarchical Reinforcement Learning

23 Oct 2018

When combined with an intrinsic motivation learning mechanism, this method learns both subgoals and skills, based on experiences in the environment.

HIERARCHICAL REINFORCEMENT LEARNING MONTEZUMA'S REVENGE

Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems

1 Jun 2018

The strong performance of DeepCS on these sparse- and dense-reward tasks suggests that encouraging intra-life novelty is an interesting, new approach for improving performance in Deep RL and motivates further research into hybridizing across-training and intra-life exploration methods.

MONTEZUMA'S REVENGE