Overview

The player controls a small blue spacecraft. The game starts in a fictional solar system with several planets to explore. If the player moves his ship into a planet, he will be taken to a side-view landscape. Unlike many other shooting games, gravity plays a fair part in Gravitar: the ship will be pulled slowly to the deadly star in the overworld, and downward in the side-view levels.

The player has five buttons: two to rotate the ship left or right, one to shoot, one to activate the thruster, and one for both a tractor beam and force field. Gravitar, Asteroids, Asteroids Deluxe and Space Duel all used similar 5-button controlling system.

In the side-view levels, the player has to destroy red bunkers that shoot constantly, and can also use the tractor beam to pick up blue fuel tanks. Once all of the bunkers are destroyed, the planet will blow up, and the player will earn a bonus. Once all planets are destroyed, the player will move onto another solar system.

The player will lose a life if he crashes into the terrain or gets hit by an enemy’s shot, and the game will end immediately if fuel runs out.

Gravitar has 12 different planets. Red Planet is available in all 3 phases in the universe; it contains a reactor. Shooting the reactor core activates a link. Escaping the reactor successfully moves the player to the next phase of planets, awards bonus points and 7500 units of fuel. Reactor escape time reduces after each phase and eventually becomes virtually impossible to complete.

After completing all 11 planets (or alternatively completing the reactor three times) the player enters the second universe and the gravity will reverse. Instead of dragging the ship towards the planet surface, the gravity pushes it away. In the third universe the landscape becomes invisible and the gravity is positive again. The final, fourth universe, has invisible landscape and reverse gravity. After completing the fourth universe the game starts over. However, the reactor escape time will never reset back to high levels again

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!

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Human Starts

Result Algorithm Source
3116.0 Human Massively Parallel Methods for Deep Reinforcement Learning
567.5 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
538.37 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
422.0 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
351.0 Prioritized DQN (rank) Prioritized Experience Replay
320.0 A3C LSTM Asynchronous Methods for Deep Reinforcement Learning
303.5 A3C FF Asynchronous Methods for Deep Reinforcement Learning
297.0 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
269.5 A3C FF 1 day Asynchronous Methods for Deep Reinforcement Learning
269.5 Prioritized DDQN (rank, tuned) Prioritized Experience Replay
245.5 Random Massively Parallel Methods for Deep Reinforcement Learning
218.0 Prioritized DDQN (prop, tuned) Prioritized Experience Replay
216.5 DQN Massively Parallel Methods for Deep Reinforcement Learning
200.5 DDQN (tuned) Deep Reinforcement Learning with Double Q-learning
170.0 DDQN Deep Reinforcement Learning with Double Q-learning
167.0 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning

No-op Starts

Result Algorithm Source
3351.4 Human Dueling Network Architectures for Deep Reinforcement Learning
2672.0 Human Human-level control through deep reinforcement learning
2209 NoisyNet DuDQN Noisy Networks for Exploration
2041.8 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
1804.8 Reactor ND The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
1682 DuDQN Noisy Networks for Exploration
1419.3 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
1073.8 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
1054.58 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
995 QR-DQN-1 Distributional Reinforcement Learning with Quantile Regression
911 IQN Implicit Quantile Networks for Distributional Reinforcement Learning
681.0 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
588.0 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
546 QR-DQN-0 Distributional Reinforcement Learning with Quantile Regression
473.0 DQN A Distributional Perspective on Reinforcement Learning
447 NoisyNet DQN Noisy Networks for Exploration
440 C51 A Distributional Perspective on Reinforcement Learning
429 Contingency Human-level control through deep reinforcement learning
412.0 DDQN A Distributional Perspective on Reinforcement Learning
387.7 Linear Human-level control through deep reinforcement learning
379 A3C Noisy Networks for Exploration
366 DQN Noisy Networks for Exploration
359.5 IMPALA (deep) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
314 NoisyNet A3C Noisy Networks for Exploration
306.7 DQN Human-level control through deep reinforcement learning
282.5 IMPALA (deep, multitask) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
238.0 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning
211.5 IMPALA (shallow) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
173.0 Random Human-level control through deep reinforcement learning
170.5 DDQN Deep Reinforcement Learning with Double Q-learning

Normal Starts

Result Algorithm Source
3906 RND Exploration by Random Network Distillation
3426 PPO Exploration by Random Network Distillation
3371 Dynamics Exploration by Random Network Distillation
737.2 PPO Proximal Policy Optimization Algorithm
225.3 ACER Proximal Policy Optimization Algorithm
194.0 A2C Proximal Policy Optimization Algorithm