Reinforcement Learning on Enduro Atari 2600 (test)
470Average Total RewardDQN
Evaluation results
| Method | Links | ||
|---|---|---|---|
| DQNpolicy=epsilon-greedy, epsilon=0.052013.12 | 470 | — | |
| Humanagent=expert human player2013.12 | 368 | — | |
| Contingencypolicy=epsilon-greedy, epsilon=0.052013.12 | 159 | — | |
| Sarsapolicy=epsilon-greedy, epsilon=0.052013.12 | 129 | — | |
| Randompolicy=random2013.12 | 0 | — | |
| DQN Bestpolicy=epsilon-greedy, epsilon=0.05, evaluation=best episode2013.12 | — | 661 | |
| HNeat Bestpolicy=deterministic2013.12 | — | 106 | |
| HNeat Pixelpolicy=deterministic, input=8 color channel representation2013.12 | — | 91 |