Fully Parameterized Quantile Function for Distributional Reinforcement Learning
This work addresses a key bottleneck in distributional RL for improving performance in environments like Atari Games, representing an incremental advancement over existing methods.
The paper tackled the challenge of parameterizing estimated distributions in distributional reinforcement learning by proposing a fully parameterized quantile function that parameterizes both the quantile fraction and value axes, resulting in significant performance improvements, including setting a new record on 55 Atari Games for non-distributed agents.
Distributional Reinforcement Learning (RL) differs from traditional RL in that, rather than the expectation of total returns, it estimates distributions and has achieved state-of-the-art performance on Atari Games. The key challenge in practical distributional RL algorithms lies in how to parameterize estimated distributions so as to better approximate the true continuous distribution. Existing distributional RL algorithms parameterize either the probability side or the return value side of the distribution function, leaving the other side uniformly fixed as in C51, QR-DQN or randomly sampled as in IQN. In this paper, we propose fully parameterized quantile function that parameterizes both the quantile fraction axis (i.e., the x-axis) and the value axis (i.e., y-axis) for distributional RL. Our algorithm contains a fraction proposal network that generates a discrete set of quantile fractions and a quantile value network that gives corresponding quantile values. The two networks are jointly trained to find the best approximation of the true distribution. Experiments on 55 Atari Games show that our algorithm significantly outperforms existing distributional RL algorithms and creates a new record for the Atari Learning Environment for non-distributed agents.