Atari Chopper Command Environment

Overview

In Chopper Command the player controls a military helicopter in a desert scenario protecting a convoy of trucks. The goal is to destroy all enemy fighter jets and helicopters that attack the player’s helicopter and the friendly trucks traveling below, ending the current wave. The game ends when the player loses all of his or her lives or reaches 999,999 points. A radar, called a Long Range Scanner in the instruction manual, shows all enemies, including those not visible on the main screen.

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
10916.0 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
10150.0 A3C LSTM Asynchronous Methods for Deep Reinforcement Learning
9600.5 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
8930.0 Human Massively Parallel Methods for Deep Reinforcement Learning
8058.0 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning
7021.0 A3C FF Asynchronous Methods for Deep Reinforcement Learning
6685.0 Prioritized DQN (rank) Prioritized Experience Replay
6604.0 Prioritized DDQN (prop, tuned) Prioritized Experience Replay
4669.0 A3C FF 1 day Asynchronous Methods for Deep Reinforcement Learning
4635.0 Prioritized DDQN (rank, tuned) Prioritized Experience Replay
3784.0 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
3495.0 DDQN (tuned) Deep Reinforcement Learning with Double Q-learning
3191.75 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
3046.0 DQN Massively Parallel Methods for Deep Reinforcement Learning
2483.0 DDQN Deep Reinforcement Learning with Double Q-learning
644.0 Random Massively Parallel Methods for Deep Reinforcement Learning

No-op Starts

Result Algorithm Source
107779.0 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
37568 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
28255.0 IMPALA (deep) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
19901.5 Reactor ND The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
16836 IQN Implicit Quantile Networks for Distributional Reinforcement Learning
16654.0 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
15600 C51 A Distributional Perspective on Reinforcement Learning
14667 QR-DQN-1 Distributional Reinforcement Learning with Quantile Regression
13185.0 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning
13136.0 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
11477 NoisyNet DuDQN Noisy Networks for Exploration
11215.0 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
9881.8 Human Human-level control through deep reinforcement learning
8893 NoisyNet DQN Noisy Networks for Exploration
7561 NoisyNet A3C Noisy Networks for Exploration
7388 DuDQN Noisy Networks for Exploration
7387.8 Human Dueling Network Architectures for Deep Reinforcement Learning
7271 DQN Noisy Networks for Exploration
7138 QR-DQN-0 Distributional Reinforcement Learning with Quantile Regression
6687 DQN Human-level control through deep reinforcement learning
6126.0 DQN A Distributional Perspective on Reinforcement Learning
5809.0 DDQN A Distributional Perspective on Reinforcement Learning
5285 A3C Noisy Networks for Exploration
5036.0 IMPALA (deep, multitask) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
5012.0 IMPALA (shallow) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
4653.0 DDQN Deep Reinforcement Learning with Double Q-learning
4167.5 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
1582 Linear Human-level control through deep reinforcement learning
811.0 Random Human-level control through deep reinforcement learning
16.9 Contingency Human-level control through deep reinforcement learning

Normal Starts

Result Algorithm Source
5287.7 ACER Proximal Policy Optimization Algorithm
3516.3 PPO Proximal Policy Optimization Algorithm
2070 DRQN Deep Recurrent Q-Learning for Partially Observable MDPs
1460 DQN Ours Deep Recurrent Q-Learning for Partially Observable MDPs
1450 DQN Ours Deep Recurrent Q-Learning for Partially Observable MDPs
1330 DRQN Deep Recurrent Q-Learning for Partially Observable MDPs
1171.7 A2C Proximal Policy Optimization Algorithm