Overview

In Fishing Derby, two fishermen sit on opposite docks over a lake filled with fish (and a shark that passes through). Using the joystick the player is able to move his line left right and up and down in the water. When a fish is hooked, the line slowly comes up to the surface of the water. Pressing the fire button on the joystick reels in the fish faster. However, if both fishermen have fish hooked, only one person can reel theirs in (the one who hooked theirs first). The shark that roams the water will try to eat hooked fish before they surface.

The objective for both fishermen is to reach 99 pounds of fish first. There are six rows of fish; the top two rows have 2 lb. fish, the middle two rows have 4 lb. fish, and the two bottom rows have 6 lb. fish. The more valuable fish sit at the bottom, but they are harder to bring in as they run a higher risk of being eaten by the shark.

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
22.6 A3C LSTM Asynchronous Methods for Deep Reinforcement Learning
22.6 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
18.8 A3C FF Asynchronous Methods for Deep Reinforcement Learning
17.0 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning
13.6 A3C FF 1 day Asynchronous Methods for Deep Reinforcement Learning
9.8 Prioritized DDQN (rank, tuned) Prioritized Experience Replay
9.2 Prioritized DDQN (prop, tuned) Prioritized Experience Replay
5.1 Human Massively Parallel Methods for Deep Reinforcement Learning
4.64 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
3.5 Prioritized DQN (rank) Prioritized Experience Replay
3.2 DDQN (tuned) Deep Reinforcement Learning with Double Q-learning
-2.3 DQN Massively Parallel Methods for Deep Reinforcement Learning
-3.4 DDQN Deep Reinforcement Learning with Double Q-learning
-4.1 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
-4.9 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
-77.1 Random Massively Parallel Methods for Deep Reinforcement Learning

No-op Starts

Result Algorithm Source
57 NoisyNet DuDQN Noisy Networks for Exploration
46.4 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
44.85 IMPALA (deep) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
41.3 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning
39.0 QR-DQN-1 Distributional Reinforcement Learning with Quantile Regression
37.4 QR-DQN-0 Distributional Reinforcement Learning with Quantile Regression
35.27 IMPALA (deep, multitask) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
35 DuDQN Noisy Networks for Exploration
33.8 IQN Implicit Quantile Networks for Distributional Reinforcement Learning
33.73 ACKTR Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
33.1 Reactor ND The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
32.08 IMPALA (shallow) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
31.3 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
30.4 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
23.2 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
20.3 DDQN Deep Reinforcement Learning with Double Q-learning
20.19 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
15.5 DDQN A Distributional Perspective on Reinforcement Learning
11 NoisyNet DQN Noisy Networks for Exploration
9.1 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
8.9 C51 A Distributional Perspective on Reinforcement Learning
5.5 Human Human-level control through deep reinforcement learning
4 DQN Noisy Networks for Exploration
-0.8 DQN Human-level control through deep reinforcement learning
-4.9 DQN A Distributional Perspective on Reinforcement Learning
-7 A3C Noisy Networks for Exploration
-38 NoisyNet A3C Noisy Networks for Exploration
-38.7 Human Dueling Network Architectures for Deep Reinforcement Learning
-85.1 Contingency Human-level control through deep reinforcement learning
-89.5 Linear Human-level control through deep reinforcement learning
-91.7 Random Human-level control through deep reinforcement learning

Normal Starts

Result Algorithm Source
34.7 ACER Proximal Policy Optimization Algorithm
20.6 A2C Proximal Policy Optimization Algorithm
17.8 PPO Proximal Policy Optimization Algorithm