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Deep reinforcement learning has obtained significant breakthroughs in recentyears. Most methods in deep-RL achieve good results via the maximization of thereward signal provided by the environment, typically in the form of discountedcumulative returns. Such reward signals represent the immediate feedback of aparticular action performed by an agent. However, tasks with sparse rewardsignals are still challenging to on-policy methods. In this paper, we introducean effective characterization of past reward statistics (which can be seen aslong-term feedback signals) to supplement this immediate reward feedback. Inparticular, value functions are learned with multi-critics supervision,enabling complex value functions to be more easily approximated in on-policylearning, even when the reward signals are sparse. We also introduce a novelexploration mechanism called "hot-wiring" that can give a boost to seeminglytrapped agents. We demonstrate the effectiveness of our advantage actormulti-critic (A2MC) method across the discrete domains in Atari games as wellas continuous domains in the MuJoCo environments. A video demo is provided athttps://youtu.be/zBmpf3Yz8tc.

Improving On-policy Learning with Statistical Reward Accumulation