Source-linked AI summary
Generalization and Regularization in DQN
Jesse Farebrother, Marlos C. Machado, Michael Bowling
TL;DR
Deep RL policies and representations often struggle to generalize across similar environments. This paper evaluates DQN with Atari game modes, finding overfitting and showing that regularization can produce reusable features whose fine-tuning improves sample efficiency, while regularization alone is insufficient for cross-flavour policy generalization.
Problem
Deep RL policies and learned representations often fail to generalize or transfer when tasks change slightly, despite deep networks’ generalization capabilities elsewhere.
Method
The paper evaluates DQN across hold-out Atari 2600 modes and difficulties, then tests dropout, ℓ2 regularization, representation reuse, and fine-tuning.
Results
DQN shows an overfitting trend, while regularization helps learn more general features that can be reused and fine-tuned to improve sample efficiency on similar tasks.
Takeaways & Limitations
Regularizing and fine-tuning representations may improve generalization and sample efficiency across multiple related tasks, including settings where environments change continually.
Takeaways & Limitations
Regularization alone does not enable policies to generalize across flavours, and the Atari setting provides only about 10 environment flavours compared with settings assuming many more variations.
Abstract
from arXiv · showhide
Deep reinforcement learning algorithms have shown an impressive ability to learn complex control policies in high-dimensional tasks. However, despite the ever-increasing performance on popular benchmarks, policies learned by deep reinforcement learning algorithms can struggle to generalize when evaluated in remarkably similar environments. In this paper we propose a protocol to evaluate generalization in reinforcement learning through different modes of Atari 2600 games. With that protocol we assess the generalization capabilities of DQN, one of the most traditional deep reinforcement learning algorithms, and we provide evidence suggesting that DQN overspecializes to the training environment. We then comprehensively evaluate the impact of dropout and $\ell_2$ regularization, as well as the impact of reusing learned representations to improve the generalization capabilities of DQN. Despite regularization being largely underutilized in deep reinforcement learning, we show that it can, in fact, help DQN learn more general features. These features can be reused and fine-tuned on similar tasks, considerably improving DQN's sample efficiency.
1 Introduction
The paper examines whether deep RL representations generalize across similar tasks and evaluates DQN using Atari game variations. It finds evidence of DQN overfitting and investigates regularization and fine-tuning as ways to improve generality.
- Research aim: The study evaluates whether deep RL representations can generalize or be reused and refined across small task variations.It applies supervised-learning techniques, including regularization and fine-tuning, to DQN.
- Definition of generalization: The paper focuses on agents that abstract environment aspects that do not matter across tasks with similar dynamics and different observation spaces.This frames generalization as reusable abstraction across related tasks.
- Evaluation protocol: Atari 2600 modes and difficulties provide a platform for training in one environment and testing in a slightly different environment.The variations preserve key concepts such as game sprites and dynamics.
- Findings: DQN exhibits an overfitting trend under this evaluation of generalization.The assessment targets tasks with related underlying structure rather than identical environments.
- Regularization: Dropout and ℓ2 regularization improve DQN’s sample efficiency on new tasks and support analysis of zero-shot generalization and fine-tuning.The paper examines whether regularization helps learn representations that transfer across problems.
2 Background
The background introduces supervised-learning regularization, reinforcement-learning objectives, DQN, and related work on generalization and representation reuse. It also positions Atari modes as a broader evaluation setting for closely related tasks.
- 2.1 Regularization in Supervised Learning: Supervised learning trains a neural network to predict outputs from examples while generalizing to unseen examples.The network is parameterized by weights θ and learns a function mapping inputs to predicted labels.
- 2.1 Regularization in Supervised Learning: ℓ2 regularization penalizes large weight vectors, with λ controlling the regularization term’s importance.The loss combines prediction quality with a regularization penalty.
- 2.1 Regularization in Supervised Learning: Dropout randomly sets neural units to zero and discourages reliance on a small number of neurons, making memorization harder.The dropout rate p is the Bernoulli probability used during forward propagation.
- 2.1 Regularization in Supervised Learning: Reusing pretrained layers transfers their encoded weights to another task, where those weights can be fine-tuned.The paper studies this reuse in a later section.
- 2.2 Reinforcement Learning: Reinforcement learning models an agent interacting with an environment to maximize cumulative long-term reward.The framework is represented as an MDP with states, actions, transition dynamics, rewards, and discounting.
- 2.2 Reinforcement Learning: Q-learning updates state-action values from environmental samples, while function approximation makes learning tractable in large state spaces.The update uses a step size α, and the learned approximation targets qπ.
- 2.2 Reinforcement Learning: DQN approximates state-action values with a neural network that maps an encoded state to |A| action-value outputs.Experience replay supplies uniformly sampled transitions, and a delayed duplicate network improves stability.
- 2.3 Related Work: Prior work has generally applied regularization in narrower settings, while this paper studies representation reuse and regularized value-function fine-tuning.Related work also examines generalization through environment noise, start-state distributions, and Atari parameterization.
3 The ALE as a Platform for Evaluating Generalization in Reinforcement Learning
The paper uses Atari 2600 game flavours as hold-out environments for testing whether agents identify invariances across similar tasks with different observations. This framework provides baseline results across multiple games and flavours while constraining task variation to a few latent environmental variables.
- Evaluation platform: Atari 2600 flavours provide hold-out environments that modify sprites, velocities, observability, or difficulty while preserving key aspects of the original game.The protocol evaluates agents trained in one flavour and tested in another, using variations native to the Atari 2600 platform.
- Evaluation platform: Generalization is defined as identifying invariances across tasks with high-dimensional observations when only a few latent environmental variables change.This perspective treats the desired agent behavior as invariance to changes in the observation space.
- Evaluation platform: The paper provides first baseline results for different Atari 2600 flavours after their incorporation into the ALE.The baselines cover multiple games and flavours and include traditional deep RL and regularization settings.
- Evaluation platform: The proposed setting treats generalization as performance across multiple related tasks rather than requiring samples from one identical distribution.Task variation is constrained so the environments remain small variations of the default game.
- Evaluation platform: The study evaluates 13 flavours from four games: Freeway, HERO, Breakout, and Space Invaders.The selected games vary vehicle dynamics, starting levels, brick observability, and other gameplay properties.
4 Generalization of the Policies Learned by DQN
The paper tests whether DQN policies trained in Atari 2600’s default flavour transfer to similar target flavours. DQN often generalizes poorly and can overfit to the training flavour, including when the underlying game dynamics remain closely related.
- Evaluation protocol: The evaluation measures cumulative reward over 100 episodes after training the epsilon-greedy policy for 50M frames in default flavour m0d0.Results are compared with a baseline trained from scratch for 50M frames in each target flavour.
- Direct policy evaluation: DQN policies trained in one flavour generally fail to generalize well to other, remarkably similar flavours.The evaluation compares a policy trained for 50M frames in default flavour m0d0 with performance in new flavours.
- Direct policy evaluation: 15.8 points versus 29.9 points: the policy trained in Freeway m0d0 achieved approximately half the scratch-trained score in m4d0.The two flavours use exactly the same sprites; m4d0 differs by cars accelerating and decelerating over time.
- Overfitting analysis: Freeway target-flavour performance displays a bell-shaped learning curve, consistent with initial transfer followed by overfitting to the training flavour.Performance in m4d0 instead flattens, while more complex flavours do not show the same prominent curve.
- Overfitting analysis: The results suggest that DQN struggles with small task variations and exhibits a troubling overfitting pattern in Freeway.The paper uses these findings to motivate evaluating regularization methods for improving generalization.
5 Regularization in DQN
The paper evaluates dropout and ℓ2 regularization as ways to improve DQN’s generalization across Atari game flavours. Regularization sometimes reduces default-task performance, but generally preserves or improves cross-flavour performance, while regularization alone remains insufficient for full policy generalization.
- Evaluation setup: The study evaluates dropout and ℓ2 regularization to test whether overfitting explains DQN’s poor generalization across game flavours.Dropout is applied to early network layers, while ℓ2 regularization is applied to all weights.
- Evaluation setup: Regularized policies are evaluated after 50M frames of default-mode training on the listed game flavours.Results are averaged over five runs, with standard deviations reported in Table 2.
- Default-task trade-off: Regularization can impose a performance hit in the default flavour because dropout generally requires more training iterations to reach comparable performance.The authors prioritize adaptability and future-task performance over maximal performance on one task.
- Cross-flavour evaluation: In most flavours, regularization does not reduce evaluation performance relative to unregularized training, and Freeway improves across all flavours.In two of three Freeway flavours, the regularized policy outperforms the baseline learned from scratch.
- Cross-flavour evaluation: Regularization alone does not seem sufficient for policies to generalize across flavours, although it can produce representations that are reused and fine-tuned on other flavours.Learning from scratch in new flavours remains more beneficial than directly reusing the regularized policy.
6 Value function fine-tuning
The paper tests whether regularization is most useful as a source of reusable initialization rather than immediate zero-shot generalization. Fine-tuning regularized representations produces stronger cross-task results and can improve sample efficiency compared with non-regularized initialization or learning from scratch.
- Fine-tuning strategy: The authors hypothesize that regularization helps by providing parameter initializations that can be adapted to similar new tasks.They fine-tune either the entire regularized network or only its early layers.
- Fine-tuning strategy: Fine-tuning experiments compare pretrained weights with non-regularized or scratch baselines using 50M and 100M frame budgets.The experiments are averaged over five seeds and assess sample efficiency across flavours.
- Entire-network fine-tuning: Non-regularized fine-tuning gives conflicting results: Freeway improves, but sparse rewards in mode 1 may make initialization act like optimistic initialization.The resulting agents still cannot reach the maximum score in these flavours.
- Entire-network fine-tuning: Regularized fine-tuning achieves the highest Freeway scores reported and outperforms non-regularized fine-tuning in HERO.Space Invaders also shows higher scores across the board at the same amount of experience.
- Sample efficiency: Regularized initialization appears better than random initialization when total frames across tasks are considered.Scratch uses 200M frames for two flavours, whereas regularized fine-tuning uses 150M frames, including 50M frames in the default flavour.
- Early-layer fine-tuning: The paper also investigates whether early layers contain general features whose co-adaptations can be relearned after later layers are randomly initialized.This tests whether state-action value layers should be relearned for each flavour.
7 Discussion and conclusion
The discussion frames Atari game modes as a testbed for generalization and concludes that DQN generalizes poorly across closely related tasks. Dropout, ℓ2 regularization, and representation fine-tuning offer a route toward more adaptable features and improved sample efficiency, with possible relevance to continual learning.
- Discussion: Generalization in reinforcement learning is complicated by multiple meanings, including conditioned sub-goals and representation learning across environments.The paper focuses on representation learning as a way to study generalization across related tasks.
- Discussion: DQN generalizes poorly even when tasks have very similar underlying dynamics.This conclusion motivates evaluating dropout and ℓ2 regularization as possible remedies.
- Discussion: Regularization has been underutilized in deep RL because evaluation commonly trains and tests agents on exactly the same task.The paper argues that closely related-task evaluation makes representation regularization relevant to deep RL.
- Discussion: The evaluated regularizers appear able to learn general-purpose features that adapt to similar problems.The discussion relates this approach to meta-learning and notes that MAML and REPTILE did not succeed in the paper’s setting.
- Conclusion: Fine-tuning regularized representations is presented as a viable approach for improving sample efficiency and generalization across multiple tasks.The authors highlight possible relevance to continual learning when environments change without warning.
Game Modes
The paper describes the game flavours used in its Atari 2600 evaluation.
- The section provides a brief description of each game flavour used in the paper.
Freeway
Freeway requires crossing moving traffic under a time limit, with modes and difficulties changing vehicle behavior and collision penalties.
- The agent is rewarded for reaching the screen’s top and then teleported back to the bottom.
- Mode 1 adds buses, increases vehicle density in some lanes, and raises vehicle speed.
- Difficulty 0 bumps a colliding chicken down one lane, whereas difficulty 1 teleports it to the starting position.
Hero
Hero tasks require navigating cave mazes to rescue a miner, while later modes begin at progressively harder levels and introduce additional challenges.
- The player navigates a maze to rescue a trapped miner, earning points for forward progress such as clearing obstacles or killing enemies.
- Rescuing the miner terminates the level and advances the agent to a new maze.
- Some levels include partially observable rooms, more enemies, and harder obstacles.
- Later modes start at increasingly harder levels, with the default beginning at level 1 and mode 1 at level 5.
Breakout
The paper evaluates DQN across Atari game flavours and studies whether dropout and ℓ2 regularization improve training stability and zero-shot generalization. In the reported Freeway ablation, reasonable dropout helps generalization, while ℓ2 benefits m1d0 more than m4d0.
- Breakout: The DQN architecture contains three convolutional layers and two fully connected layers, with baseline hyperparameters kept consistent across experiments.
- Breakout: Each result averages performance over 100 episodes and five runs without taking maxima across runs or learning curves.
- Breakout: The ablation trains on Freeway m0d0 for 50M frames and evaluates checkpoints on m1d0 and m4d0.
- Breakout: ℓ2 regularization improves m1d0 evaluation but provides little apparent benefit in m4d0, while combining regularizers can increase training stability.The selected balance was pconv, pfc = 0.05, 0.1 and λ = 10^-4.
- Breakout: Reasonable dropout rates improve generalization in m1d0 and m4d0, whereas larger rates can flatline training and evaluation.