Overview

Gameplay is on a flat plane with a mountainous horizon featuring an erupting volcano, distant crescent moon, and various geometric solids (in vector outline) like pyramids and blocks. The player views the screen, which includes an overhead radar view, to find and destroy the rather slow tanks, or the faster-moving supertanks. Saucer-shaped UFOs and guided missiles occasionally appear for a bonus score. The saucers differ from the tanks in that they do not fire upon the player and do not appear on radar. The player can hide behind the solids or, once fired upon, maneuver in rapid turns to buy time with which to fire.

The geometric solid obstacles are indestructible, and can block the movement of a player’s tank. However, they are also useful as shields as they block enemy fire as well.

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
306500 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning
52040.0 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
33030.0 Human Massively Parallel Methods for Deep Reinforcement Learning
32250.0 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
31320.0 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
29100.0 Prioritized DDQN (prop, tuned) Prioritized Experience Replay
25520.0 Prioritized DDQN (rank, tuned) Prioritized Experience Replay
25240.0 DDQN Deep Reinforcement Learning with Double Q-learning
24740.0 DDQN (tuned) Deep Reinforcement Learning with Double Q-learning
22250.0 Prioritized DQN (rank) Prioritized Experience Replay
20760.0 A3C LSTM Asynchronous Methods for Deep Reinforcement Learning
19938.0 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
17560.0 DQN Massively Parallel Methods for Deep Reinforcement Learning
12950.0 A3C FF Asynchronous Methods for Deep Reinforcement Learning
11340.0 A3C FF 1 day Asynchronous Methods for Deep Reinforcement Learning
3560.0 Random Massively Parallel Methods for Deep Reinforcement Learning

No-op Starts

Result Algorithm Source
98235.0 Reactor ND The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
64070.0 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
62010.0 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
61220 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
52262 NoisyNet DuDQN Noisy Networks for Exploration
42244 IQN Implicit Quantile Networks for Distributional Reinforcement Learning
41145.0 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
40481 DuDQN Noisy Networks for Exploration
39268 QR-DQN-1 Distributional Reinforcement Learning with Quantile Regression
37800.0 Human Human-level control through deep reinforcement learning
37187.5 Human Dueling Network Architectures for Deep Reinforcement Learning
37150.0 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
36786 NoisyNet DQN Noisy Networks for Exploration
35580 QR-DQN-0 Distributional Reinforcement Learning with Quantile Regression
35520.0 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning
31700.0 DDQN A Distributional Perspective on Reinforcement Learning
29900.0 DQN A Distributional Perspective on Reinforcement Learning
28981 DQN Noisy Networks for Exploration
28742 C51 A Distributional Perspective on Reinforcement Learning
26300 DQN Human-level control through deep reinforcement learning
25730.0 DDQN Deep Reinforcement Learning with Double Q-learning
25266.66 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
20885.0 IMPALA (deep) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
17871 NoisyNet A3C Noisy Networks for Exploration
16411 A3C Noisy Networks for Exploration
15820 Linear Human-level control through deep reinforcement learning
13015.0 IMPALA (shallow) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
8910.0 ACKTR Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
7705.0 IMPALA (deep, multitask) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
2360.0 Random Human-level control through deep reinforcement learning
16.2 Contingency Human-level control through deep reinforcement learning

Normal Starts

Result Algorithm Source
17366.7 PPO Proximal Policy Optimization Algorithm
8983.3 ACER Proximal Policy Optimization Algorithm
3080.0 A2C Proximal Policy Optimization Algorithm