Overview

The goal of Venture is to collect treasure from a dungeon. Winky is equipped with a bow and arrow and explores a dungeon with rooms and hallways. The hallways are patrolled by large, tentacled monsters named Hallmonsters, which cannot be killed, injured, or stopped in any way. Once in a room, Winky may kill monsters, avoid traps and gather treasures. If they stay in any room too long, a Hallmonster will enter the room, chase and kill them. In this way, the Hallmonsters serve the same role as “Evil Otto” in the arcade game Berzerk. The more quickly the player finishes each level, the higher their score.

The goal of each room is only to steal the room’s treasure. In most rooms, it is possible (though difficult) to steal the treasure without defeating the monsters within. Some rooms have traps that are only sprung when the player picks up the treasure. For instance, in “The Two-Headed Room”, two 2-headed ettins appear the moment the player picks up the prize.

Winky dies if he touches a monster or Hallmonster. Dead monsters decay over time and their corpses may block room exits, delaying Winky and possibly allowing the Hallmonster to enter. Shooting a corpse causes it to regress back to its initial death phase. The monsters themselves move in specific patterns but may deviate to chase the player, and the game’s AI allows them to dodge the player’s shots with varying degrees of “intelligence” (for example, the snakes of “The Serpent Room” are relatively slow to dodge arrows, the trolls of “The Troll Room” are quite adept at evasion).

The game consists of three different dungeon levels with different rooms. After clearing all the rooms in a level the player advances to the next. After three levels the room pattern and monsters repeat, but at a higher speed and with a different set of treasures.

The different dungeons in each level are as follows:

  • Level 1 - The Wall Room, The Serpent Room, The Skeleton Room, The Goblin Room
  • Level 2 - The Two-Headed Room, The Dragon Room, The Spider Room, The Troll Room
  • Level 3 - The Genie Room, The Demon Room, The Cyclops Room, The Bat Room

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
1039.0 Human Massively Parallel Methods for Deep Reinforcement Learning
523.4 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
462.0 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
244.0 Prioritized DDQN (prop, tuned) Prioritized Experience Replay
200.0 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
110.0 Prioritized DQN (rank) Prioritized Experience Replay
94.0 Prioritized DDQN (rank, tuned) Prioritized Experience Replay
75.0 DDQN Deep Reinforcement Learning with Double Q-learning
54.0 DQN Massively Parallel Methods for Deep Reinforcement Learning
45.0 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
29.0 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning
25.0 A3C LSTM Asynchronous Methods for Deep Reinforcement Learning
23.0 A3C FF Asynchronous Methods for Deep Reinforcement Learning
21.0 DDQN (tuned) Deep Reinforcement Learning with Double Q-learning
19.0 A3C FF 1 day Asynchronous Methods for Deep Reinforcement Learning
18.0 Random Massively Parallel Methods for Deep Reinforcement Learning

No-op Starts

Result Algorithm Source
1653.5 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
1597.5 Reactor The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
1520 C51 A Distributional Perspective on Reinforcement Learning
1433 DuDQN Noisy Networks for Exploration
1318 IQN Implicit Quantile Networks for Distributional Reinforcement Learning
1245.33 Gorila DQN Massively Parallel Methods for Deep Reinforcement Learning
1187.5 Human Dueling Network Architectures for Deep Reinforcement Learning
1187.5 Human Human-level control through deep reinforcement learning
1107.0 Distributional DQN Rainbow: Combining Improvements in Deep Reinforcement Learning
815 NoisyNet DuDQN Noisy Networks for Exploration
497.0 DDQN A Distributional Perspective on Reinforcement Learning
497.0 DuDQN Dueling Network Architectures for Deep Reinforcement Learning
380.0 DQN Human-level control through deep reinforcement learning
319 DQN Noisy Networks for Exploration
163.0 DQN A Distributional Perspective on Reinforcement Learning
97 NoisyNet DQN Noisy Networks for Exploration
93.0 DDQN Deep Reinforcement Learning with Double Q-learning
66 Linear Human-level control through deep reinforcement learning
48.0 PDD DQN Dueling Network Architectures for Deep Reinforcement Learning
43.9 QR-DQN-1 Distributional Reinforcement Learning with Quantile Regression
5.5 Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning
1.0 IMPALA (deep, multitask) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
0.6 Contingency Human-level control through deep reinforcement learning
0.0 Random Human-level control through deep reinforcement learning
0 A3C Noisy Networks for Exploration
0 NoisyNet A3C Noisy Networks for Exploration
0.0 QR-DQN-0 Distributional Reinforcement Learning with Quantile Regression
0.0 Reactor ND The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
0.0 IMPALA (shallow) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
0.0 IMPALA (deep) IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

Normal Starts

Result Algorithm Source
1859 RND Exploration by Random Network Distillation
1712 Dynamics Exploration by Random Network Distillation
0 PPO Exploration by Random Network Distillation
0.0 A2C Proximal Policy Optimization Algorithm
0.0 ACER Proximal Policy Optimization Algorithm
0.0 PPO Proximal Policy Optimization Algorithm