[Paper Review] Recurrent Environment Simulators

Silvia Chiappa, Sébastien Racanière+2 more|arXiv (Cornell University)|Apr 7, 2017

Reinforcement Learning in Robotics19 references105 citations

TL;DR

The paper presents recurrent environment simulators that predict long-horizon dynamics of environments from high-dimensional observations, introduces action-conditioned state transitions, and analyzes training schemes to balance short- and long-term accuracy across diverse domains. It also introduces a prediction-independent variant to improve efficiency and demonstrates use for model-based exploration.

ABSTRACT

Models that can simulate how environments change in response to actions can be used by agents to plan and act efficiently. We improve on previous environment simulators from high-dimensional pixel observations by introducing recurrent neural networks that are able to make temporally and spatially coherent predictions for hundreds of time-steps into the future. We present an in-depth analysis of the factors affecting performance, providing the most extensive attempt to advance the understanding of the properties of these models. We address the issue of computationally inefficiency with a model that does not need to generate a high-dimensional image at each time-step. We show that our approach can be used to improve exploration and is adaptable to many diverse environments, namely 10 Atari games, a 3D car racing environment, and complex 3D mazes.

Motivation & Objective

• Motivate the need for accurate, temporally and spatially coherent environment models for planning and planning-based exploration.
• Develop recurrent, action-conditioned simulators that operate on high-dimensional perceptual inputs.
• Systematically analyze how training schemes (prediction-dependent vs observation-dependent transitions) affect short- and long-term accuracy.
• Introduce a prediction-independent variant to reduce computational cost when long-horizon predictions are of interest.
• Demonstrate the approach across diverse environments and explore implications for model-based exploration.

Proposed method

• Extend the Oh et al. (2015) recurrent simulator with an action-conditioned backbone that integrates actions directly into state transitions.
• Introduce a prediction-dependent transition framework and an optional prediction-independent variant to avoid expensive high-dimensional generation during multi-step prediction.
• Formalize state updates with an LSTM-based backbone and convolutional encoders/decoders for processing observations.
• Experiment with Atari 2600 games, randomly generated 3D mazes, and TORCS car racing to assess temporal and spatial coherence over hundreds of steps.
• Systematically evaluate training schemes that vary warm-up length, prediction horizon, and the proportion of prediction-dependent transitions (PDT) to study long-term versus short-term accuracy.
• Utilize truncated backpropagation through time to handle longer horizons when needed.

Experimental results

Research questions

• RQ1 How can action-conditioned recurrent models produce temporally and spatially coherent predictions over long horizons from high-dimensional inputs?
• RQ2 What is the impact of different training schemes (prediction-dependent vs observation-dependent transitions) on short-term and long-term predictive accuracy?
• RQ3 Does incorporating actions directly into state transitions improve modeling of environment dynamics?
• RQ4 Can prediction-independent variants maintain useful predictive performance while reducing computational cost?
• RQ5 How well do these simulators support model-based exploration across varied environments (Atari, 3D mazes, TORCS)?

Key findings

• Prediction-dependent transitions improve long-term accuracy, especially for complex environments, at the cost of short-term sharpness.
• Observation-dependent transitions alone yield poor long-term performance in most games; mixing strategies can be preferable in very complex settings.
• Higher prediction horizons (longer T) generally boost long-term accuracy when PDT schemes are used, but may reduce short-term quality.
• Backpropagation through time can be truncated to manage long horizons, with multi-subsequence schemes sometimes increasing long-term accuracy.
• A prediction-independent variant can dramatically reduce computation when predicting many steps ahead, by avoiding high-dimensional image generation at each step.
• Models trained with richer long-term accuracy can generalize to human play in interactive simulations, though may be more sensitive to policies unseen during training.