Papers › Count-Based Exploration with Neural Density Models
Count-Based Exploration with Neural Density Models
Georg Ostrovski, Marc G. Bellemare, Aaron van den Oord, Remi Munos
Bellemare et al. (2016) introduced the notion of a pseudo-count, derived from a density model, to generalize count-based exploration to non-tabular reinforcement learning. This pseudo-count was used to generate an exploration bonus for a DQN agent and combined with a mixed Monte Carlo update was sufficient to achieve state of the art on the Atari 2600 game Montezuma's Revenge. We consider two questions left open by their work: First, how important is the quality of the density model for exploration? Second, what role does the Monte Carlo update play in exploration? We answer the first question by demonstrating the use of PixelCNN, an advanced neural density model for images, to supply a pseudo-count. In particular, we examine the intrinsic difficulties in adapting Bellemare et al.'s approach when assumptions about the model are violated. The result is a more practical and general algorithm requiring no special apparatus. We combine PixelCNN pseudo-counts with different agent architectures to dramatically improve the state of the art on several hard Atari games. One surprising finding is that the mixed Monte Carlo update is a powerful facilitator of exploration in the sparsest of settings, including Montezuma's Revenge.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Atari Games | Atari 2600 Freeway | DQN-CTS | Score | 33.0 | #20 of 59 | Archive leaderboard | report |
| Atari Games | Atari 2600 Freeway | DQN-PixelCNN | Score | 31.7 | #29 of 59 | Archive leaderboard | report |
| Atari Games | Atari 2600 Gravitar | DQN-PixelCNN | Score | 498.3 | #31 of 53 | Archive leaderboard | report |
| Atari Games | Atari 2600 Gravitar | DQN-CTS | Score | 238.0 | #51 of 53 | Archive leaderboard | report |
| Atari Games | Atari 2600 Montezuma's Revenge | DQN-PixelCNN | Score | 3705.5 | #9 of 50 | Archive leaderboard | report |
| Atari Games | Atari 2600 Private Eye | DQN-PixelCNN | Score | 8358.7 | #12 of 52 | Archive leaderboard | report |
| Atari Games | Atari 2600 Private Eye | DQN-CTS | Score | 206.0 | #35 of 52 | Archive leaderboard | report |
| Atari Games | Atari 2600 Venture | DQN-PixelCNN | Score | 82.2 | #34 of 55 | Archive leaderboard | report |
| Atari Games | Atari 2600 Venture | DQN-CTS | Score | 48.0 | #38 of 55 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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