Overview

In Crazy Climber the player assumes the role of a person attempting to climb to the top of four skyscrapers. The climber is controlled via two joysticks.. There are a number of obstacles and dangers to avoid including:

  • Windows that open and close (the most common danger).
  • Bald-headed residents (a.k.a. Mad Doctors), who throw objects such as flower pots, buckets of water and fruit in an effort to knock the climber off the building (with larger objects appearing by more aggressive Mad Doctors in later levels).
  • A giant condor, who drops eggs and excrement aimed at the climber (two at a time in the early stages, four in later levels).
  • A giant ape (styled like King Kong), whose punch can prove deadly (he becomes more aggressive in later levels).
  • Falling steel girders and iron dumbbells (more numerous in the later levels).
  • Live wires, which protrude off electric signs.
  • Falling ‘Crazy Climber’ signs.

Some of these dangers appear at every level of the game; others make appearances only in later stages. Should the climber succumb to any one of these dangers, a new climber takes his place at the exact point where he fell; the last major danger is eliminated.

One ally the climber has is a pink “Lucky Balloon”; if he is able to grab it, the climber is transported up 8 stories to a window. The window onto which it drops the climber may be about to close. If the window that the climber is dropped onto is fully closed, the balloon pauses there until the window opens up again. The player does not actually earn bonus points for catching the balloon, but he is awarded the normal ‘step value’ for each of the eight floors that he passes while holding the balloon.

If the climber is able to ascend to the top of a skyscraper and grabs the runner of a waiting helicopter, he earns a bonus and is transported to another skyscraper, which presents more dangers than the past. The helicopter would only wait about 30 seconds, then fly off.

If the player completes all four skyscrapers, he is taken back to the first skyscraper and the game restarts from the beginning, but the player keeps his score.

The difficulty level of any game was modified to take into account the skill of previous players. Hence if a player pushed the high score up to say 250,000 (needed a really good player), any novice player following would get thoroughly wiped out for several games due to the increased difficulty level, and would have to play until it dropped back down.

Description from Wikipedia

Performances of RL Agents

We list various reinforcement learning algorithms that were tested in this environment. These results are from RL Database. If this page was helpful, please consider giving a star!

Star

Human Starts

Result Algorithm Source
154416.5 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
143962.0 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
138518.0 A3C LSTM Asynchronous Methods for Deep Reinforcement Learning
131086.0 Prioritized DDQN (prop, tuned) Prioritized Experience Replay
127853.0 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning
127512.0 Prioritized DDQN (rank, tuned) Prioritized Experience Replay
124566.0 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
113782.0 DDQN (tuned) Deep Reinforcement Learning with Double Q-learning
112646.0 A3C FF Asynchronous Methods for Deep Reinforcement Learning
109337.0 Prioritized DQN (rank) Prioritized Experience Replay
101624.0 A3C FF 1 day Asynchronous Methods for Deep Reinforcement Learning
94315.0 DDQN Deep Reinforcement Learning with Double Q-learning
65451.0 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
50992.0 DQN Massively Parallel Methods for Deep Reinforcement Learning
32667.0 Human Massively Parallel Methods for Deep Reinforcement Learning
9337.0 Random Massively Parallel Methods for Deep Reinforcement Learning

No-op Starts

Result Algorithm Source
236422.0 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
194347 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
181233 QR-DQN-0 Distributional Reinforcement Learning with Quantile Regression
179877 C51 A Distributional Perspective on Reinforcement Learning
179082 IQN Implicit Quantile Networks for Distributional Reinforcement Learning
178355.0 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
173274.0 Reactor ND The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
171171 NoisyNet DuDQN Noisy Networks for Exploration
168788.5 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
163335 DuDQN Noisy Networks for Exploration
162224.0 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning
161196 QR-DQN-1 Distributional Reinforcement Learning with Quantile Regression
150444.0 ACKTR Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
143570.0 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
139950 NoisyNet A3C Noisy Networks for Exploration
136950.0 IMPALA (deep) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
136211.5 IMPALA (shallow) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
134783 A3C Noisy Networks for Exploration
118305 NoisyNet DQN Noisy Networks for Exploration
117282.0 DDQN A Distributional Perspective on Reinforcement Learning
116480 DQN Noisy Networks for Exploration
115384.0 IMPALA (deep, multitask) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
114103 DQN Human-level control through deep reinforcement learning
110763.0 DQN A Distributional Perspective on Reinforcement Learning
101874.0 DDQN Deep Reinforcement Learning with Double Q-learning
85919.16 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
35829.4 Human Dueling Network Architectures for Deep Reinforcement Learning
35410.5 Human Human-level control through deep reinforcement learning
23411 Linear Human-level control through deep reinforcement learning
10780.5 Random Human-level control through deep reinforcement learning
149.8 Contingency Human-level control through deep reinforcement learning

Normal Starts

Result Algorithm Source
132461.0 ACER Proximal Policy Optimization Algorithm
110202.0 PPO Proximal Policy Optimization Algorithm
107770.0 A2C Proximal Policy Optimization Algorithm