Source-linked AI summary
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Kimin Lee, Younggyo Seo, Seunghyun Lee, Honglak Lee, Jinwoo Shin
TL;DR
Model-based RL struggles to generalize when environment transition dynamics change. CaDM separates context encoding from transition prediction, trains the context with forward and backward dynamics objectives, and improves generalization across simulated control tasks and model-free policies.
Problem
Model-based RL must learn accurate forward dynamics for test environments whose transition dynamics differ from those seen during training.
Method
CaDM encodes recent experience into a context latent vector and conditions forward transition prediction on it, while training the vector with forward and backward dynamics objectives.
Results
CaDM reduces the performance gap between training and unseen test environments and improves model-free RL generalization across simulated control tasks.
Takeaways & Limitations
Separating context encoding from transition inference provides a generalizable dynamics model whose learned context can also support model-free policies.
Takeaways & Limitations
Evaluation assumes the reward function is known when using model-predictive control.
Abstract
from arXiv · showhide
Model-based reinforcement learning (RL) enjoys several benefits, such as data-efficiency and planning, by learning a model of the environment's dynamics. However, learning a global model that can generalize across different dynamics is a challenging task. To tackle this problem, we decompose the task of learning a global dynamics model into two stages: (a) learning a context latent vector that captures the local dynamics, then (b) predicting the next state conditioned on it. In order to encode dynamics-specific information into the context latent vector, we introduce a novel loss function that encourages the context latent vector to be useful for predicting both forward and backward dynamics. The proposed method achieves superior generalization ability across various simulated robotics and control tasks, compared to existing RL schemes.
1. Introduction
Model-based RL is data-efficient but can fail when transition dynamics change across environments. CaDM addresses this by separating context encoding from transition prediction and training the context to support forward and backward dynamics, improving generalization across simulated tasks.
- Model-based RL can produce data-efficient planning, but small transition-dynamics changes can make next-state predictions inaccurate and degrade performance.
- Existing generalization strategies include meta-learning and graph networks, but simple hidden-state or gradient updates may not capture rich environmental context.
- CaDM separates context encoding from transition inference, producing a latent vector from recent experience and conditioning the forward dynamics model on it.
- Its loss trains the latent vector to support both forward and backward predictions while encouraging temporal consistency across nearby future timesteps.
- Across simulated control tasks, CaDM reduces the training–test performance gap against ensemble and model-based meta-RL baselines.
2. Related Work
Related work studies dynamics generalization in model-based and model-free RL through graph networks, meta-learning, and context-conditioned policies. CaDM differs by training its context encoder directly through dynamics prediction.
- Model-based RL learns an environment dynamics model for planning or improving model-free data efficiency, but generalization remains a longstanding challenge.
- The paper evaluates dynamics changes in CartPole, Pendulum, and HalfCheetah by varying environment parameters such as pole length, pendulum length, and body mass.
- Prior model-based approaches use graph networks or meta-learning to adapt dynamics models to changed environments.
- Model-free approaches improve robustness through adversarial training, structured policies, online system identification, and meta-learning.
- Unlike a context method trained by maximizing expected returns, CaDM trains its context encoder for dynamics prediction.
3. Problem Statement
The problem is to learn a forward dynamics model that remains accurate when transition dynamics vary across environments. Training uses observed contexts, while evaluation targets related test environments with unseen contexts.
- The RL setting uses an MDP with state, action, transition, reward, discount, and initial-state components, and learns a forward model approximating transition dynamics.
- The transition dynamics vary according to an environment context, such as damage that changes a robot’s behavior.
- The goal is accurate prediction in test environments with unseen contexts sampled from a test distribution distinct from training contexts.
4. Context-aware Dynamics Model
CaDM separates dynamics-specific context encoding from transition inference and trains a shared latent vector with forward, backward, and multi-step prediction objectives. The learned context can also condition model-free policies and improve their generalization.
- Context encoding and transition inference: CaDM encodes dynamics-specific information into a latent vector, then predicts the next state conditioned on that vector.
- Context encoder and dynamics model: The forward model predicts the next state, while the backward model predicts the current state from the next state, action, and shared context latent vector.
- Model compatibility: The method is architecture-agnostic and can improve stochastic dynamics models when the models are conditioned on the learned context.
- Prediction losses: The training objective uses forward and backward prediction tasks plus future-step predictions to capture context and support longer-horizon modeling.
- History of transitions: The context encoder can use recent transition histories represented by state differences rather than raw states.
- Combination with model-free RL: The learned context latent vector can condition a model-free policy, which outperforms existing context-conditional policies in generalization ability.
5. Experiments
The experiments evaluate CaDM across changing-dynamics control environments, comparing model-based and model-free settings, prediction accuracy, and contextual representations. CaDM improves generalization and prediction performance, especially in complex environments, while its gains are marginal in simple ones.
- Experimental questions: Experiments evaluate robustness to dynamics changes, model-free integration, prediction loss variants, prediction accuracy, and contextual representations.The study uses simulated robots and classic control tasks, sampling parameters from training ranges during training and unseen ranges at test time.
- Setups: Test environments use parameters outside the training range, with moderate and extreme regimes differing by their distance from the training range.Results report mean and standard deviation across five runs, selecting models by highest average training return.
- Model-based RL comparison: CaDM significantly improves training and test performance across environments, with larger gains in complex long-horizon and high-dimensional domains.The reported tasks include HalfCheetah, Ant, CrippledHalfCheetah, SlimHumanoid, CartPole, and Pendulum.
- Model-based RL comparison: 2019.6 to 7087.2: combining CaDM with PE-TS improves average return for HalfCheetah in the moderate regime.This result illustrates that CaDM can be combined with different model-based methods; stacking transitions sometimes degrades training and test performance.
- Model-free RL comparison: CaDM improves generalization over prior conditional policy methods in complex environments, while gains are marginal in CartPole and Pendulum.The comparison uses PPO + CaDM against PPO + EP and PPO + PC.
- Prediction and representation analysis: CaDM maintains tolerable prediction errors beyond the HalfCheetah training range, separates context embeddings by mass, and predicts distant future states more accurately.Vanilla DM and Stacked DM become inaccurate at more distant future timesteps, whereas Vanilla + CaDM remains accurate across them.
6. Conclusion
The paper proposes a context-aware dynamics model that adapts to changing dynamics by separating context encoding from transition inference. Its context vector supports forward and backward dynamics prediction and improves generalization for model-free methods.
- CaDM adapts to dynamics changes by separating context encoding from transition inference.
- The method learns contextual information by forcing the latent vector to support both forward and backward dynamics prediction.
- The learned context vector also improves the generalization performance of model-free methods.
Supplementary Material
The supplementary material describes the environments, task objectives, observation and action spaces, rewards, and dynamics modifications used in the experiments.
- Environments: The experiments include CartPole, Pendulum, Half-cheetah, Ant, and SlimHumanoid environments with distinct control objectives.
- Task objectives: CartPole uses cart pushing to keep the pole upright, while Pendulum requires swinging a body upright.
- Dynamics modifications: Dynamics are varied by modifying masses, lengths, push force, or damping across the tested environments.
- Robot specifications: Half-cheetah and Ant observations contain joint kinematics and torso information, with six- and eight-joint torque actions respectively.
- Rewards: The task rewards combine forward motion or upright stability with penalties for control effort or state deviation.
A.2. Training Details
Training details specify data collection, optimization, planning, and evaluation procedures for model-based and model-free RL experiments.
- Model-based RL: Model-based RL collects 10 MPC-controlled trajectories, trains for 5 epochs per iteration, and runs 20 iterations per experiment.
- Model-based RL: Model-based RL uses Adam with learning rate 0.001 and evaluates test performance every iteration with fixed random seeds.
- Model-free RL: Model-free RL trains PPO for 5 million timesteps on Pendulum and MuJoCo environments and 0.5 million timesteps on CartPole.
- Model-free RL: PPO evaluation occurs every 10,000 timesteps using fixed random seeds, discount factor γ = 0.99, and generalized advantage estimation.
A.3. Implementation of Context-aware Dynamics Model
The implementation section specifies CaDM and the comparison baselines, including context-vector construction, observation stacking, adaptive models, ensembles, and context-conditioned policies.
- CaDM: CaDM uses a three-layer MLP context encoder that produces a 10-dimensional vector concatenated with state and action inputs.
- CaDM: The CaDM implementation varies penalty β over {0.25, 0.5, 1.0} and uses 5 or 10 past and future observations.
- Baselines: Vanilla DM uses a four-layer Gaussian dynamics model, while Stacked DM and Stacked PPO receive 10 additional observations.
- Baselines: GrBAL and ReBAL use adaptive batch modeling and recurrent modeling, with planning horizons of 10 or 30 and learning rate 0.01.
- Baselines: PE-TS uses five bootstrap models and 20 trajectory-sampling particles, while PPO context baselines encode 10-dimensional Gaussian context vectors.
B. Effects of Training Environments
Wider and varying training environments improve CaDM’s generalization, while Vanilla DM does not show the same improvement. Across unseen environments, CaDM generally outperforms Vanilla DM, and the section compares dynamics-model, model-free, and objective variants.
- Training-range effects: CaDM’s CartPole generalization improves as the training range widens, whereas Vanilla DM’s performance does not improve.This suggests CaDM uses contextual information when trained across a wider range of environments.
- Unseen-environment performance: Across varying CartPole simulation parameters, performance degrades as parameters move away from the training range, but CaDM generally remains ahead of Vanilla DM.The comparison uses average returns and reports means with standard deviations across five runs in Figure 9 and means across three runs in Figure 10.
- Cross-task evaluation: The evaluation compares trained dynamics models on CartPole, Pendulum, HalfCheetah, Ant, CrippledHalfCheetah, and SlimHumanoid under moderate and extreme dynamics shifts.Moderate test environments use parameters closer to, but different from, the training range than extreme environments.
- Additional comparisons: The section also evaluates model-free methods on the same changing-dynamics tasks and compares variants of the proposed prediction objective.The model-free comparisons cover the six control tasks, while objective variants are evaluated on the same task groups.
F. Prediction Error
Prediction errors are evaluated across six control tasks while simulation parameters vary. The results are summarized using means and standard deviations across three runs.
- Task coverage: Prediction errors are measured on CartPole, Pendulum, HalfCheetah, Ant, CrippledHalfCheetah, and SlimHumanoid with varying simulation parameters.The figure presents one panel for each control task.
- Reporting: The prediction-error curves report the mean and standard deviation across three runs.This summarizes variability across repeated experiments as simulation parameters change.
G. Embedding Analysis
Embedding analyses visualize context latent vectors and raw state-action vectors collected from trajectories across control tasks. Colors identify environments sharing the same parameter.
- Context embeddings: t-SNE and PCA visualize context latent vectors extracted from trajectories collected in various control tasks.Embedded points from environments with the same parameter share a color in both visualization methods.
- Raw representations: t-SNE and PCA also visualize raw state-action vectors from the same kind of trajectory data.The same-parameter color encoding is used for the raw state-action representations.