Source-linked AI summary
Assessing Generalization in Deep Reinforcement Learning
Charles Packer, Katelyn Gao, Jernej Kos, Philipp Krähenbühl, Vladlen Koltun, Dawn Song
TL;DR
Deep RL lacks a controlled, systematic assessment of how well algorithms generalize across environments. The paper introduces a reproducible benchmark and protocol spanning diverse dynamics and interpolation and extrapolation tests, finding that vanilla algorithms generally outperform specialized schemes, with important condition-dependent exceptions.
Problem
Deep RL research lacks a common testbed and systematic empirical study with clearly defined environments, metrics, and baselines for comparing generalization methods.
Method
The paper benchmarks A2C, PPO, EPOpt, and RL2 across six dynamics-varied environments and nine training-testing scenarios covering default, interpolation, and extrapolation.
Results
Vanilla A2C and PPO generally generalized better than EPOpt and RL2, while EPOpt-PPO improved interpolation and extrapolation only in particular settings.
Takeaways & Limitations
Training vanilla algorithms on varied environments can yield fairly successful interpolation, whereas specialized methods are sensitive to their base algorithm, architecture, and environment.
Takeaways & Limitations
The effectiveness of EPOpt and RL2 was sensitive to the base algorithm, architecture, and environment, despite their presentation as general-purpose techniques.
Abstract
from arXiv · showhide
Deep reinforcement learning (RL) has achieved breakthrough results on many tasks, but agents often fail to generalize beyond the environment they were trained in. As a result, deep RL algorithms that promote generalization are receiving increasing attention. However, works in this area use a wide variety of tasks and experimental setups for evaluation. The literature lacks a controlled assessment of the merits of different generalization schemes. Our aim is to catalyze community-wide progress on generalization in deep RL. To this end, we present a benchmark and experimental protocol, and conduct a systematic empirical study. Our framework contains a diverse set of environments, our methodology covers both in-distribution and out-of-distribution generalization, and our evaluation includes deep RL algorithms that specifically tackle generalization. Our key finding is that `vanilla' deep RL algorithms generalize better than specialized schemes that were proposed specifically to tackle generalization.
1. Introduction
Deep RL is typically evaluated on fixed environments, leaving its ability to generalize to unseen environments insufficiently assessed. The paper addresses this gap with a reproducible benchmark and systematic comparison of generalization algorithms.
- Deep RL is often trained and evaluated on one fixed environment rather than tested for representations that generalize to unseen circumstances.
- Subtle environmental changes can expose overfitting and sensitivity in deep RL algorithms.
- Generalization includes interpolation to similar environments and extrapolation beyond the training distribution, with extrapolation especially challenging for real-world deployment.
- Existing studies use differing environments, variations, and experimental protocols, preventing controlled comparisons of generalization methods.
- The paper establishes a reproducible framework focused on dynamics changes, using diverse environments and evaluating both vanilla and specialized algorithms.
- Vanilla A2C and PPO generalized better overall than EPOpt and RL2, although EPOpt improved some PPO results under specific conditions.
2. Related work
Prior work approaches RL generalization through robust policies or adaptive agents, but studies differ substantially in settings and evaluation. This paper distinguishes its broader treatment of tasks, interpolation, extrapolation, and generalization algorithms.
- Robust-policy approaches optimize performance under adverse or risk-sensitive environment variations, including EPOpt’s focus on low-reward environments.
- Adaptive approaches learn environment-specific embeddings from trajectories so agents can adjust to the dynamics encountered at test time.
- Robust and adaptive approaches generally avoid updating the learned policy or model during testing, unlike transfer, multi-task, and meta-learning work.
- Deep RL research benefits from shared simulation infrastructure such as OpenAI Gym, which this work builds upon.
- Earlier empirical studies often use fixed environments or narrower distribution shifts, whereas this work examines more tasks, extrapolation, interpolation, and specialized algorithms.
3. Notation
The paper formulates RL environments as Markov Decision Processes and defines trajectories, policies, rewards, and value functions within that framework. Generalization methods extend the objective to a distribution of environments.
- An MDP is represented by (S, A, p, r, γ, ρ0, T), covering states, actions, transitions, rewards, discounting, initial states, and episode horizon.
- A policy maps state sequences stochastically to actions, while transitions and rewards generate trajectories across time.
- Standard RL learns a policy for a fixed MDP by maximizing expected per-episode reward.
- Value functions represent expected reward from a state, and state-action value functions represent expected reward from an initial state-action pair.
- Generalization algorithms assume a distribution q(M) of environments and seek policies that maximize expected reward across that distribution.
4. Algorithms
The study compares vanilla A2C and PPO with EPOpt’s robust optimization and RL2’s recurrent adaptation strategy. These methods are evaluated under a common protocol to assess generalization across environment variations.
- The benchmark evaluates A2C, PPO, EPOpt-A2C, EPOpt-PPO, RL2-A2C, and RL2-PPO as representatives of vanilla, robust, and adaptive approaches.
- Preliminary tests with A3C, TRPO, and ACKTR produced qualitatively similar results to the evaluated algorithms.
- A2C: A2C alternates between a critic learning values and an actor learning a policy that maximizes expected reward.
- PPO: PPO maximizes a surrogate expected-reward objective while clipping policy updates to constrain each iteration’s improvement.
- EPOpt: EPOpt maximizes expected reward over the ε fraction of environments with the worst expected policy performance, targeting robustness to distribution shifts.
- RL2: RL2 uses a recurrent policy and value network whose hidden state embeds trajectory information to adapt to the current environment dynamics.
5. Environments
The benchmark modifies six classic-control and robotic environments so dynamics vary across deterministic, random, and extreme versions, supporting tests of interpolation and extrapolation.
- Environment suite: The benchmark includes modified CartPole, MountainCar, Acrobot, Pendulum, HalfCheetah, and Hopper environments with controllable dynamics parameters.Each environment has three versions, with d parameters allowed to vary.
- Environment versions: Deterministic environments fix parameters at default values, while random environments resample them uniformly from a box around those defaults.These versions represent fixed training and feasible distributions of training environments, respectively.
- Environment versions: Extreme environments sample parameters from boxes anchored at the random-version box’s vertices, representing unusual edge cases outside training.The extreme ranges are intended to test deployment on environments not seen during training.
- Environment parameters: The environments vary task-relevant dynamics, including forces, masses, lengths, inertias, and robot power-related parameters.CartPole, MountainCar, and Acrobot use discrete actions, while Pendulum, HalfCheetah, and Hopper include continuous control settings.
- Performance metric: Success rate measures the percentage of episodes completing predefined goals, providing an interpretable metric independent of reward shaping.The goals include balancing, reaching a target, stabilizing a pendulum, or walking 20 meters.
6. Experimental methodology
The study evaluates six algorithms across nine training-testing combinations for six environments, using default, interpolation, and extrapolation metrics under controlled network architectures.
- Benchmark design: Six algorithms are benchmarked across six environments and nine combinations of deterministic, random, and extreme training and testing versions.The algorithms are A2C, PPO, EPOpt-A2C, EPOpt-PPO, RL2-A2C, and RL2-PPO.
- Evaluation metrics: Default is success on DD, Interpolation is success on RR, and Extrapolation is the geometric mean of success on DR, DE, and RE.These metrics summarize performance in fixed, similar, and distribution-shifted environments.
- Architectures: The evaluation uses feed-forward MLP and recurrent architectures, with recurrent inputs differing for RL2 algorithms.The feed-forward networks use two 64-unit hidden layers, while the recurrent design uses an LSTM-based feature network.
- Training protocol: Hyperparameters are selected by grid search using success probability on the same environment version used for training.The searched settings include learning rate and trajectory length, among other optimizer parameters.
7. Results and discussion
Vanilla A2C and PPO generally generalized better than specialized EPOpt and RL2 schemes, although outcomes varied substantially by algorithm, architecture, and environment.
- A2C and PPO: Vanilla A2C and PPO usually interpolated fairly successfully, despite performance declines from Default to Interpolation to Extrapolation.For PPO, Default equals 78.22, Interpolation equals 70.57, and Extrapolation equals 48.37.
- Environment variation: PPO with the feed-forward architecture achieved 85.54% Default success on Hopper but only 39.68% Interpolation and 10.36% Extrapolation.The contrast illustrates that strong fixed-environment performance did not ensure generalization.
- EPOpt: EPOpt-PPO improved interpolation and extrapolation over PPO in some environments but performed worse in the other three environments.Its gains appeared on Pendulum, Hopper, and HalfCheetah, and depended on combining EPOpt with PPO and continuous action spaces.
- EPOpt: EPOpt-A2C generally failed to train successfully, while EPOpt-PPO also struggled under the recurrent architecture and on fixed environments.The reported effectiveness of EPOpt was therefore conditional on algorithm, architecture, and environment.
- RL2: RL2-A2C and RL2-PPO were difficult to train and data inefficient, with low Default, Interpolation, and Extrapolation values on most environments.A notable exception was RL2-A2C on Pendulum, with Interpolation 99.82 and Extrapolation 81.79.
- RL2: Adding variation to a fixed environment sometimes stabilized training enough for an algorithm to find a better policy for the original environment.The paper also suggests that useful environmental characteristics may require additional structure in the policy.
8. Conclusion
The paper introduces a common testbed and protocol for comparing deep RL generalization across similar and different environments. Overall, vanilla A2C and PPO generalize better than more complex schemes, although EPOpt can help in specific settings and RL2 is difficult to train.
- The study evaluates A2C, PPO, EPOpt, and RL2 using a testbed and protocol covering generalization to environments both similar to and different from training environments.
- Vanilla A2C and PPO achieve better overall generalization than their more complex counterparts, with fairly strong interpolation and some extrapolation success.
- EPOpt outperforms PPO when combined with PPO under the FF architecture, particularly on continuous-action environments, but does not generalize consistently elsewhere.
- RL2 is difficult to train and, in successful cases, provides no clear generalization advantage over vanilla algorithms or EPOpt.
- The effectiveness of EPOpt and RL2 is sensitive to the base algorithm, architecture, and environment, despite their presentation as general-purpose techniques.
A. Training Hyperparameters
The study uses a broad hyperparameter search and evaluates generalization across a large set of train/test configurations. The protocol reports results over five runs of the full grid search.
- The search space contains 183 unique hyperparameter configurations for all algorithms on one training environment.
- The grid search comprises 3,294 training configurations and evaluates each trained agent on three test settings, totaling 9,882 train/test configurations.
- Results are reported for five runs of the full grid search, totaling 49,410 experiments.
- The listed hyperparameter settings include trajectory lengths of [5, 10, 15] for A2C and RL2-A2C and [128, 256, 512] for PPO and RL2-PPO.
- The KL divergence coefficient is searched over [0.3, 0.2, 0.0].
C. Behavior of MountainCar
MountainCar generalization results can favor extrapolation over interpolation because the extrapolation set includes easy, extreme parameter regions. Pendulum shows the opposite ordering, highlighting environment-specific behavior.
- On MountainCar, several algorithms achieve higher success on Extrapolation than Interpolation, which can exceed Default.
- Extrapolation combines DR, DE, and RE, while Interpolation is the success rate on RR.
- The higher MountainCar rewards in E are associated with regions having large force, allowing the car to reach the hilltop in fewer than 110 time steps.
- On Pendulum, Interpolation exceeds Extrapolation, with the policy trained on D struggling more on environments from E than from R.
- The contrasting MountainCar and Pendulum patterns show why generalization assessments should include a wide variety of environments.
- MountainCar heatmaps plot A2C FF rewards across the two environment parameters varied in R and E.
D. Varying N in RL2
The RL2 sensitivity study finds that increasing the number of episodes per trial usually harms generalization, with two environment-and-algorithm exceptions. At N = 1, RL2 also underperforms the comparable recurrent-context baseline on average.
- Increasing N usually degrades generalization performance, suggesting that longer trajectories make training more difficult.
- At N = 1, RL2-A2C and RL2-PPO match their RC-architecture counterparts while additionally receiving actions, rewards, and done flags.
- At N = 1, average RL2 generalization is lower than the comparable RC architecture; Interpolation is 66.83 for RL2-A2C versus 72.22 for A2C with RC.
E. Training Curves
Training curves compare PPO, EPOpt-PPO, and RL2-PPO across environment versions, while videos illustrate how training conditions affect learned HalfCheetah behavior. Increased randomness often stabilizes training, and gait changes track environmental difficulty.
- E. Training Curves: Training curves average five experiment runs and show standard-deviation error bands for PPO, EPOpt-PPO, and RL2-PPO across environment versions.
- E. Training Curves: In most cases, increased randomness in the Random and Extreme environments appears to stabilize training, including when successful policies are found.
- E. Training Curves: HalfCheetah videos provide a case study of behavior learned by agents across training conditions.
- E. Training Curves: Across several algorithms, HalfCheetah agents develop a forward-leaning gait in Random and Extreme environments but remain relatively flat in Deterministic training.
- E. Training Curves: Figure 4’s EPOpt-PPO reward decrease at 10000 episodes reflects a switch from using all generated episodes to the 10% with lowest reward.
- E. Training Curves: Figures 5 and 6 show video frames for A2C and PPO agents trained in Deterministic, Random, and Extreme HalfCheetah environments.