Frugal Actor-Critic: Sample Efficient Off-Policy Deep Reinforcement
  Learning Using Unique Experiences
Nikhil Kumar Singh Indranil Saha
Efficient utilization of the replay buffer plays a significant role in the
off-policy actor-critic reinforcement learning (RL) algorithms used for
model-free control policy synthesis for complex dynamical systems. We propose a
method for achieving sample efficiency, which focuses on selecting unique
samples and adding them to the replay buffer during the exploration with the
goal of reducing the buffer size and maintaining the independent and
identically distributed (IID) nature of the samples. Our method is based on
selecting an important subset of the set of state variables from the
experiences encountered during the initial phase of random exploration,
partitioning the state space into a set of abstract states based on the
selected important state variables, and finally selecting the experiences with
unique state-reward combination by using a kernel density estimator. We
formally prove that the off-policy actor-critic algorithm incorporating the
proposed method for unique experience accumulation converges faster than the
vanilla off-policy actor-critic algorithm. Furthermore, we evaluate our method
by comparing it with two state-of-the-art actor-critic RL algorithms on several
continuous control benchmarks available in the Gym environment. Experimental
results demonstrate that our method achieves a significant reduction in the
size of the replay buffer for all the benchmarks while achieving either faster
convergent or better reward accumulation compared to the baseline algorithms.