Papers › Mean Actor Critic
Mean Actor Critic
Cameron Allen, Kavosh Asadi, Melrose Roderick, Abdel-rahman Mohamed, George Konidaris, Michael Littman
We propose a new algorithm, Mean Actor-Critic (MAC), for discrete-action continuous-state reinforcement learning. MAC is a policy gradient algorithm that uses the agent's explicit representation of all action values to estimate the gradient of the policy, rather than using only the actions that were actually executed. We prove that this approach reduces variance in the policy gradient estimate relative to traditional actor-critic methods. We show empirical results on two control domains and on six Atari games, where MAC is competitive with state-of-the-art policy search algorithms.
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Code
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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 Beam Rider | MAC | Score | 6072 | #38 of 49 | Archive leaderboard | report |
| Atari Games | Atari 2600 Breakout | MAC | Score | 372.7 | #30 of 58 | Archive leaderboard | report |
| Atari Games | Atari 2600 Pong | MAC | Score | 10.6 | #47 of 52 | Archive leaderboard | report |
| Atari Games | Atari 2600 Q*Bert | MAC | Score | 243.4 | #54 of 57 | Archive leaderboard | report |
| Atari Games | Atari 2600 Seaquest | MAC | Score | 1703.4 | #43 of 57 | Archive leaderboard | report |
| Atari Games | Atari 2600 Space Invaders | MAC | Score | 1173.1 | #44 of 55 | Archive leaderboard | report |
| Continuous Control | Cart Pole (OpenAI Gym) | MAC | Score | 178.3 | #1 of 1 | Archive leaderboard | report |
| Continuous Control | Lunar Lander (OpenAI Gym) | MAC | Score | 163.5 | #5 of 5 | 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.
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