Overview

The gopher tunnels left and right and up to the surface. When he makes a hole to the surface he will attempt to steal a carrot. The farmer must hit the gopher to send him back underground or fill in the holes to prevent him from reaching the surface. If gopher has taken any of the three carrots, a pelican will occasionally fly overhead and drop a seed which, if the farmer catches it, he can plant it in the place of the missing carrot. The longer the game, the faster the gopher gets. The game ends when the gopher successfully removes all three carrots. There are two skill levels and is for one or two players, giving a total of four game variations.

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
105148.4 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning
72595.7 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
57783.8 Prioritized DDQN (prop, tuned) Prioritized Experience Replay
34858.8 Prioritized DDQN (rank, tuned) Prioritized Experience Replay
27778.3 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
20051.4 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
17478.2 Prioritized DQN (rank) Prioritized Experience Replay
17106.8 A3C LSTM Asynchronous Methods for Deep Reinforcement Learning
15253.0 DDQN (tuned) Deep Reinforcement Learning with Double Q-learning
10022.8 A3C FF Asynchronous Methods for Deep Reinforcement Learning
8742.8 DDQN Deep Reinforcement Learning with Double Q-learning
8442.8 A3C FF 1 day Asynchronous Methods for Deep Reinforcement Learning
4373.04 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
2731.8 DQN Massively Parallel Methods for Deep Reinforcement Learning
2311.0 Human Massively Parallel Methods for Deep Reinforcement Learning
250.0 Random Massively Parallel Methods for Deep Reinforcement Learning

No-op Starts

Result Algorithm Source
118365 IQN Implicit Quantile Networks for Distributional Reinforcement Learning
118050 QR-DQN-0 Distributional Reinforcement Learning with Quantile Regression
113585 QR-DQN-1 Distributional Reinforcement Learning with Quantile Regression
104368.2 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning
89851.0 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
70354.6 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
69135.1 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
66782.3 IMPALA (deep) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
47730.8 ACKTR Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
38909 NoisyNet DuDQN Noisy Networks for Exploration
36279.1 Reactor ND The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
33641 C51 A Distributional Perspective on Reinforcement Learning
28841.0 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
27313 DuDQN Noisy Networks for Exploration
15718.4 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
14840.8 DDQN A Distributional Perspective on Reinforcement Learning
14574 NoisyNet DQN Noisy Networks for Exploration
12439 NoisyNet A3C Noisy Networks for Exploration
11825 DQN Noisy Networks for Exploration
8777.4 DQN A Distributional Perspective on Reinforcement Learning
8520 DQN Human-level control through deep reinforcement learning
8215.4 DDQN Deep Reinforcement Learning with Double Q-learning
7992 A3C Noisy Networks for Exploration
5279.0 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
2412.5 Human Dueling Network Architectures for Deep Reinforcement Learning
2368 Contingency Human-level control through deep reinforcement learning
2321.0 Human Human-level control through deep reinforcement learning
1288 Linear Human-level control through deep reinforcement learning
1002.4 IMPALA (shallow) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
913.5 IMPALA (deep, multitask) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
257.6 Random Human-level control through deep reinforcement learning

Normal Starts

Result Algorithm Source
37802.3 ACER Proximal Policy Optimization Algorithm
2932.9 PPO Proximal Policy Optimization Algorithm
1500.9 A2C Proximal Policy Optimization Algorithm