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Recurrent Environment Simulators

Silvia Chiappa, Sébastien Racaniere, Daan Wierstra, Shakir Mohamed

arXiv:1704.02254v2cs.AIcs.LGstat.ML

TL;DR

The paper addresses the need for accurate, coherent, and efficient long-horizon environment simulation for agent planning and action. It introduces recurrent simulators with alternative architectures and training schemes, including prediction-independent state transitions, and finds improved long-term prediction alongside a short-term accuracy trade-off. The approach is evaluated across Atari, 3D mazes, and car racing and supports model-based exploration.

  • Problem

    Agents need models that anticipate action consequences over extended horizons, but existing simulators face long-term prediction, computational-efficiency, and understanding challenges.

  • Method

    The paper develops recurrent action-conditional simulators with alternative architectures and training schemes, including state transitions that can avoid predicting high-dimensional images at every step.

  • Results

    The simulators achieve state-of-the-art Atari results, adapt across Atari, 3D mazes, and car racing, and support model-based exploration; more prediction-dependent training improves long-term accuracy while reducing short-term accuracy.

  • Takeaways & Limitations

    Environment simulators can capture complex long-term interactions across diverse environments and can be used for model-based exploration.

  • Takeaways & Limitations

    The approach is deterministic and designed for deterministic environments, so extending it to noisy state transitions requires future generative temporal models.

Abstract

from arXiv · show

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.

1 INTRODUCTION

Environment simulators let agents anticipate action consequences for planning, but existing systems remain difficult to understand, long-horizon, and computationally inefficient. This paper develops improved architectures and training schemes, including a simulator that avoids predicting images after every action.

  • Agents require environment models to anticipate how actions change environments over extended future horizons.
  • Useful simulators should provide accurate, temporally and spatially coherent long-term predictions while supporting flexible action sequences and diverse high-dimensional environments.
  • Deep neural simulators enabled high-dimensional visual prediction, with Oh et al. (2015) reporting high long-term accuracy in deterministic discrete-action environments.
  • Open challenges include poorly understood generalisation and training sensitivity, difficult long-horizon prediction, and computational inefficiency from generating a high-dimensional image after each action.
  • The paper develops alternative architectures and training schemes, introduces image-sparing simulation, evaluates three environment families, and applies the simulators to model-based exploration.The evaluated families are Atari 2600 games, randomly generated 3D mazes, and 3D car racing.

2 RECURRENT ENVIRONMENT SIMULATORS

The paper formulates environment simulation as recurrent action-conditional prediction and modifies the recurrent structure so actions can directly control hidden-state transitions. This enables prediction-independent simulation, which avoids repeated high-dimensional encoding and decoding during multi-action rollouts.

  • An environment simulator predicts the effects of subsequent actions from prior actions and observations, producing future observations or state representations.
  • The baseline recurrent simulator uses a hidden state, deterministic transition function, observation or predicted-frame inputs, and convolutional encoding and decoding.
  • Training minimizes mean squared error between observed and predicted future time-series, using stochastic-gradient mini-batches of sub-sampled temporal segments.
  • The proposed architecture lets actions condition hidden-state transitions directly instead of influencing them only indirectly through observations or predictions.
  • SHORT-TERM VERSUS LONG-TERM ACCURACY: Training analysis finds that more prediction-dependent transitions improve long-term accuracy but can reduce short-term accuracy, favoring mixed schemes for some complex games.
  • ACTION-DEPENDENT STATE TRANSITION: A prediction-independent simulator evolves state as st = f(st−1, at−1) without using frames or predictions during usage.
  • ACTION-DEPENDENT STATE TRANSITION: Prediction-independent rollouts improve computational efficiency for action sequences by avoiding repeated projections between lower-dimensional states and higher-dimensional observations.

3 PREDICTION-DEPENDENT SIMULATORS

The analysis shows that training with more consecutive prediction-dependent transitions generally improves long-term accuracy, but often reduces short-term accuracy and visual sharpness. Results across Atari and 3D environments also show that truncated backpropagation, state-transition design, and task complexity affect simulator quality.

  • 3.1 ATARI: Increasing consecutive prediction-dependent transitions improves long-term accuracy, usually at the expense of short-term accuracy and sharpness.Observation-dependent-only training performs poorly in most games, while complex games may require mixed schemes.
  • 3.1 ATARI: In Seaquest, more prediction-dependent transitions better capture game dynamics, including new fish appearing in the correct location, but make fish less sharp.This trade-off makes mixed training schemes preferable for Breakout, Ms Pacman, Qbert, and Space Invaders.
  • 3.1 ATARI: Increasing prediction length T improves long-term accuracy with prediction-dependent transitions, but can reduce short-term accuracy and visual sharpness.For 100%PDT, gains are usually clearer from T = 10 to T = 15 than from T = 15 to T = 20.
  • 3.1 ATARI: Using multiple truncated-backpropagation subsequences improves long-term accuracy for 100%PDT, but can sharply reduce short-term accuracy, especially with five subsequences.For Riverraid, multiple subsequences dramatically improve long-term prediction after a jet loss under 33%PDT and 100%PDT.
  • 3.1 ATARI: Interactive human-action rollouts showed accurate game dynamics in Pong and Breakout and coherent behavior in TORCS over hundreds of frames.The Pong and Breakout rollouts updated scores correctly; TORCS handled the car hitting the track wall.
  • 3.2 3D ENVIRONMENTS: The simulator produced accurate or reasonably accurate predictions hundreds of steps ahead in TORCS and 200 steps ahead in randomly generated 3D mazes.TORCS predictions captured background features, car motion, and instrument behavior, while maze predictions retained nearby wall layout and ceiling lighting.

4 PREDICTION-INDEPENDENT SIMULATORS

The prediction-independent simulator avoids generating high-dimensional visual predictions at every step, reducing computation, but its accuracy is more sensitive to architecture and training choices. Longer truncated backpropagation can alleviate its long-term prediction weakness, although it may still underperform the prediction-dependent simulator overall.

  • Architecture: The prediction-independent simulator uses state transitions that depend only on the previous state and action, avoiding high-dimensional image prediction.In Atari, this avoids projecting between a 1,024-dimensional state space and a 100,800-dimensional observation space, saving around 200 million flops per time-step.
  • Sensitivity: The simulator is more sensitive than the prediction-dependent simulator to changes in state-transition structure and training scheme.
  • Long-term accuracy: With prediction length T = 15, the prediction-independent simulator has much worse long-term accuracy than the prediction-dependent simulator.Using T = 30 through truncated backpropagation can alleviate this problem.
  • Prediction quality: The prediction-independent simulator often produces worse long-term predictions, including spurious smaller fish in Fishing Derby, but performs better than the prediction-dependent simulator on some difficult games.
  • Evaluation: Figure 12 compares prediction-dependent and prediction-independent simulators across nine Atari games using BPTT(15, 2).

5 DISCUSSION

The approach adapts across Atari, 3D racing, and maze environments while modeling complex, long-term interactions with spatial and temporal coherence. Analysis reveals a trade-off: prediction-focused training improves long-term forecasts but can hurt short-term performance and robustness, while deterministic modeling limits applicability to noisy environments.

  • The simulator adapts across Atari games, 3D car racing, and randomly generated 3D mazes.
  • It captures complex, long-term interactions while producing spatially and temporally coherent predictions.
  • Reducing reliance on past observations focuses model resources on global dynamics and dramatically improves long-term predictions.This resource allocation can harm short-term performance in some games and reduce robustness to unseen states.
  • Compositional environments with independently moving objects may require specialized functional forms and memory stores beyond homogeneous deep networks.The paper identifies Ms Pacman as an example where the presented architecture is not optimal.
  • Policy-induced data distributions can bias learning toward action sequences that co-occur with events rather than their real causes.The limitation arises because the policy-induced distribution does not cover the full state space and may undersample important regions.
  • The approach is deterministic and therefore designed for deterministic environments, whereas noisy real-world transitions require more generative temporal models.

A DATA, PREPROCESSING AND TRAINING ALGORITHM

The Atari data pipeline repeats each action for four time-steps, records only the fourth frame, normalizes RGB inputs, and trains recurrent state updates with RMSProp-based optimization.

  • DATA, PREPROCESSING AND TRAINING ALGORITHM: Each selected action is repeated for 4 time-steps, and only the 4th frame is recorded for analysis.
  • DATA, PREPROCESSING AND TRAINING ALGORITHM: RGB images are normalized by channel-specific mean subtraction and division by 255.The means are computed separately for each color channel over an initial set of 2048 frames.
  • DATA, PREPROCESSING AND TRAINING ALGORITHM: The stochastic-gradient optimizer is centered RMSProp with learning rate 1e-5, epsilon 0.01, momentum 0.9, decay 0.95, and mini-batch size 16.
  • DATA, PREPROCESSING AND TRAINING ALGORITHM: The single-step simulator baseline uses a stated state-transition rule before the recurrent gate, cell, and state updates.

B.1 ATARI

Atari experiments use large DQN-generated datasets with epsilon-greedy actions and a convolutional encoder-decoder pipeline. Successful predictions are demonstrated at 100 and 200 time-steps for each game.

  • ATARI: The DQN agent generates 5,000,000 training images and 1,000,000 test images using an ϵ = 0.2-greedy policy.The images have dimensions 210×160 RGB, and the large training set is used to reduce overfitting.
  • ATARI: The encoder uses four convolutional layers with 64, 32, 32, and 32 filters, followed by RReLU activations and a 2816-dimensional latent vector.
  • ATARI: Successful predictions are shown at time-steps 100 and 200 for each game.

B.1.1 SHORT-TERM VERSUS LONG-TERM ACCURACY

Prediction-dependent training improves long-term modeling in several games but can trade off short-term sharpness or fail on difficult dynamics. Performance varies substantially by game and training scheme.

  • Bowling is easy to model, but prediction-dependent transitions reduce errors in score updates and ball-direction prediction.
  • Breakout exposes a short-term versus long-term trade-off: prediction-dependent training models paddle movement accurately but often fails to represent the ball.
  • Fishing Derby has disastrous long-term accuracy with 0%PDT but good long-term accuracy with 100%PDT, while observation-dependent schemes perform better short-term.
  • 100%PDT rarely loses the chicken in Freeway, although both training schemes often update the score incorrectly during the warning phase.
  • Ms Pacman and Space Invaders permit accurate prediction for only a few future time-steps because their dynamics are difficult to model.
  • In Qbert, 0%PDT predicts only the background after a few frames, while more prediction-dependent transitions reduce the sharpness of agents and moving objects.

B.1.2 DIFFERENT ACTION-DEPENDENT STATE TRANSITIONS

The study compares alternative action-dependent recurrent state transitions and finds no broadly superior replacement for the baseline. A higher-dimensional hidden representation helps specifically in Fishing Derby.

  • The experiments compare baseline and alternative transitions using unconstrained or convolutional transformations, including different gate and cell-update designs.
  • No alternative state transition is clearly preferable to the baseline across the evaluated games.
  • Fishing Derby performs better and converges earlier with transitions using 2816-dimensional hidden representations.
  • The convolutional alternatives reshape 2816-dimensional vectors into 32 × 11 × 8 tensors before applying one or two convolutions.

B.1.3 ACTION INCORPORATION

Action-dependent state transitions generally outperform action-independent transitions in the harder games, while the tested frame-augmentation approach performs considerably worse than other action-integration structures.

  • The action-incorporation experiments compare multiplicative, additive, frame-input, and combined state-and-frame interactions under T = 15 and 0%-100%PDT training.
  • The CA structure, which combines the action with the frame during encoding, is generally considerably worse than the other tested structures.
  • Action-independent transitions generally perform worse in games with higher prediction error and struggle to predict moving objects in Seaquest.
  • Action-independent transitions also make score updates more difficult in Seaquest and Fishing Derby.
  • Figures 30-35 report prediction error averaged over 10,000 sequences for Bowling and Breakout across action-dependent transition variants and parameter updates.

B.2 3D CAR RACING

The simulator produces common high-quality predictions for long action sequences in 3D car racing, but its interactive behavior is limited by actions absent from training data.

  • The car-racing data uses 10 and one million 180×180 RGB images for training and testing, respectively.
  • The car-racing environment uses accelerate-straight, accelerate-left, accelerate-right, and an added do-nothing action during random action generation.
  • Predicted and real car-racing frames are shown side by side for up to 200 actions, and this prediction quality was found to be common.
  • When selecting no action, the simulated car slightly slows down but fails to stop because extended accelerator release was absent from the training data.

B.4 MODEL-BASED EXPLORATION

Model-based exploration benefits from limiting Monte Carlo search and action-sequence depth: additional simulations quickly yield diminishing returns, while overly long sequences make predictions noisier and exploration trajectories less smooth.

  • Beyond 100 Monte Carlo simulations, exploration showed little to no improvement because additional simulations quickly produced diminishing returns.The passage attributes this to the large number of possible actions and the resulting breadth of the search space.
  • The simulator generated salient Breakout frames from 350 frames under actions taken by a human player.
  • 6-step action sequences had lower average prediction error than 30-step sequences, with errors of 0.16 and 0.37 respectively.Average minimum and maximum distances changed little, indicating that longer simulations added more noise than signal.
  • Explorer trajectories were much smoother than those selected by the random baseline.

C PREDICTION-INDEPENDENT SIMULATORS

The prediction-independent simulator is evaluated through alternative action-dependent state transitions and prediction lengths. Long-term performance depends strongly on the transition structure and training horizon, with the best reported configuration combining a specialized high-dimensional transition with two 15-step subsequences.

  • C PREDICTION-INDEPENDENT SIMULATORS: The comparison evaluates alternative action-dependent state transitions and prediction lengths T for the prediction-independent simulator.
  • Base transition: The Base–z_t−1 = h_t−1 structure uses different warm-up and prediction matrices, 1024-dimensional h_t−1 and 2048-dimensional v_t, and about 40M parameters.
  • Qualitative evaluation: The recurrent model can produce temporally extended predicted frames for TORCS, illustrated over a 200-time-step video.
  • State transitions: The Base–z_t−1 = 0 transition performs poorly for long-term prediction, while the h_t−1–i_z 2816–z_t−1 = 0 structure performs best but has many parameters.The best-performing structure uses a larger recurrent state and separate gating for the encoded frame.
  • Prediction length: Longer prediction lengths dramatically improve long-term performance, with the best overall result obtained using two subsequences of length T = 15.
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