Atari Bowling Environment

Overview

The game is based on the game of bowling, playable by one player or two players alternating.

In all six variations, games last for 10 frames, or turns. At the start of each frame, the current player is given two chances to roll a bowling ball down an alley in an attempt to knock down as many of the ten bowling pins as possible. The bowler (on the left side of the screen) may move up and down his end of the alley to aim before releasing the ball. In four of the game’s six variations, the ball can be steered before it hits the pins. Knocking down every pin on the first shot is a strike, while knocking every pin down in both shots is a spare. The player’s score is determined by the number of pins knocked down in all 10 frames, as well as the number of strikes and spares acquired.

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
146.5 Human Massively Parallel Methods for Deep Reinforcement Learning
76.8 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
69.6 DDQN (tuned) Deep Reinforcement Learning with Double Q-learning
65.8 Prioritized DDQN (prop, tuned) Prioritized Experience Replay
65.7 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
62.3 DDQN Deep Reinforcement Learning with Double Q-learning
58.0 Prioritized DQN (rank) Prioritized Experience Replay
53.95 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
52.0 Prioritized DDQN (rank, tuned) Prioritized Experience Replay
50.4 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning
41.8 A3C LSTM Asynchronous Methods for Deep Reinforcement Learning
41.2 DQN Massively Parallel Methods for Deep Reinforcement Learning
39.4 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
36.2 A3C FF 1 day Asynchronous Methods for Deep Reinforcement Learning
35.2 Random Massively Parallel Methods for Deep Reinforcement Learning
35.1 A3C FF Asynchronous Methods for Deep Reinforcement Learning

No-op Starts

Result Algorithm Source
160.7 Human Dueling Network Architectures for Deep Reinforcement Learning
154.8 Human Human-level control through deep reinforcement learning
86.5 IQN Implicit Quantile Networks for Distributional Reinforcement Learning
85.3 QR-DQN-0 Distributional Reinforcement Learning with Quantile Regression
81.8 C51 A Distributional Perspective on Reinforcement Learning
81.0 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
77.2 QR-DQN-1 Distributional Reinforcement Learning with Quantile Regression
75.4 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
74.1 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
72 DuDQN Noisy Networks for Exploration
71 NoisyNet DQN Noisy Networks for Exploration
70.5 DDQN Deep Reinforcement Learning with Double Q-learning
68.1 DDQN A Distributional Perspective on Reinforcement Learning
68 NoisyNet DuDQN Noisy Networks for Exploration
65.5 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
62 DQN Noisy Networks for Exploration
59.92 IMPALA (deep) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
59.3 Reactor ND The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
54.01 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
50.4 DQN A Distributional Perspective on Reinforcement Learning
46.7 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning
43.9 Linear Human-level control through deep reinforcement learning
42.4 DQN Human-level control through deep reinforcement learning
42 NoisyNet A3C Noisy Networks for Exploration
37 A3C Noisy Networks for Exploration
36.4 Contingency Human-level control through deep reinforcement learning
35.73 IMPALA (shallow) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
31.06 IMPALA (deep, multitask) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
30.0 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
24.3 ACKTR Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
23.1 Random Human-level control through deep reinforcement learning

Normal Starts

Result Algorithm Source
72 DQN Ours Deep Recurrent Q-Learning for Partially Observable MDPs
65.5 DRQN Deep Recurrent Q-Learning for Partially Observable MDPs
62 DRQN Deep Recurrent Q-Learning for Partially Observable MDPs
57.3 DQN Ours Deep Recurrent Q-Learning for Partially Observable MDPs
40.1 PPO Proximal Policy Optimization Algorithm
33.3 ACER Proximal Policy Optimization Algorithm
30.1 A2C Proximal Policy Optimization Algorithm