Reinforcement Learning with Latent Flow
This work provides a more effective way to incorporate temporal information for pixel-based reinforcement learning, which is a significant improvement for researchers and practitioners working on complex control tasks from raw visual input.
This paper introduces Flare, a network architecture for Reinforcement Learning that explicitly encodes temporal information through latent vector differences. Flare achieves optimal performance in state-based RL without explicit velocity access, and state-of-the-art performance on pixel-based continuous control tasks, outperforming prior model-free methods by 1.9X and 1.5X on 500k and 1M step benchmarks, respectively. It also outperforms Rainbow DQN on 5 of 8 Atari games.
Temporal information is essential to learning effective policies with Reinforcement Learning (RL). However, current state-of-the-art RL algorithms either assume that such information is given as part of the state space or, when learning from pixels, use the simple heuristic of frame-stacking to implicitly capture temporal information present in the image observations. This heuristic is in contrast to the current paradigm in video classification architectures, which utilize explicit encodings of temporal information through methods such as optical flow and two-stream architectures to achieve state-of-the-art performance. Inspired by leading video classification architectures, we introduce the Flow of Latents for Reinforcement Learning (Flare), a network architecture for RL that explicitly encodes temporal information through latent vector differences. We show that Flare (i) recovers optimal performance in state-based RL without explicit access to the state velocity, solely with positional state information, (ii) achieves state-of-the-art performance on pixel-based challenging continuous control tasks within the DeepMind control benchmark suite, namely quadruped walk, hopper hop, finger turn hard, pendulum swing, and walker run, and is the most sample efficient model-free pixel-based RL algorithm, outperforming the prior model-free state-of-the-art by 1.9X and 1.5X on the 500k and 1M step benchmarks, respectively, and (iv), when augmented over rainbow DQN, outperforms this state-of-the-art level baseline on 5 of 8 challenging Atari games at 100M time step benchmark.