  The practical usage of reinforcement learning agents is often bottlenecked by
the duration of training time. To accelerate training, practitioners often turn
to distributed reinforcement learning architectures to parallelize and
accelerate the training process. However, modern methods for scalable
reinforcement learning (RL) often tradeoff between the throughput of samples
that an RL agent can learn from (sample throughput) and the quality of learning
from each sample (sample efficiency). In these scalable RL architectures, as
one increases sample throughput (i.e. increasing parallelization in IMPALA),
sample efficiency drops significantly. To address this, we propose a new
distributed reinforcement learning algorithm, IMPACT. IMPACT extends IMPALA
with three changes: a target network for stabilizing the surrogate objective, a
circular buffer, and truncated importance sampling. In discrete action-space
environments, we show that IMPACT attains higher reward and, simultaneously,
achieves up to 30% decrease in training wall-time than that of IMPALA. For
continuous control environments, IMPACT trains faster than existing scalable
agents while preserving the sample efficiency of synchronous PPO.
