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| + | https://arxiv.org/pdf/1704.00756.pdf Multi-Advisor Reinforcement Learning | ||
| + | https://arxiv.org/pdf/1709.00503v1.pdf Mean Actor Critic | ||
| + | |||
| + | 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. This | ||
| + | significantly reduces variance in the gradient updates and removes the need for a | ||
| + | variance reduction baseline. We show empirical results on two control domains | ||
| + | where MAC performs as well as or better than other policy gradient approaches, | ||
| + | and on five Atari games, where MAC is competitive with state-of-the-art policy | ||
| + | search algorithms. | ||
