Neural Episodic Control (1703.01988v1)

Abstract: Deep reinforcement learning methods attain super-human performance in a wide range of environments. Such methods are grossly inefficient, often taking orders of magnitudes more data than humans to achieve reasonable performance. We propose Neural Episodic Control: a deep reinforcement learning agent that is able to rapidly assimilate new experiences and act upon them. Our agent uses a semi-tabular representation of the value function: a buffer of past experience containing slowly changing state representations and rapidly updated estimates of the value function. We show across a wide range of environments that our agent learns significantly faster than other state-of-the-art, general purpose deep reinforcement learning agents.

• The paper introduces Neural Episodic Control, enhancing data efficiency in deep reinforcement learning by leveraging immediate, memory-based updates.
• It employs a convolutional neural network with a Differentiable Neural Dictionary to integrate on-policy and off-policy, N-step Q-learning techniques.
• Empirical results on Atari benchmarks demonstrate NEC outperforms traditional methods, achieving high performance with significantly fewer interactions.

Neural Episodic Control: A Paradigm for Efficient Learning in Deep Reinforcement Learning Agents

The paper "Neural Episodic Control" presents a significant advancement in the domain of deep reinforcement learning (DRL) by introducing an innovative approach to enhance data efficiency—Neural Episodic Control (NEC). The authors propose this methodology as a direct response to the limitations of conventional DRL methods, which require orders of magnitude more data compared to humans to achieve comparable performance levels.

Core Contributions

The paper delineates three primary constraints that hinder the data efficiency of traditional DRL agents:

1. Stochastic Optimization Constraints: The use of small learning rates in gradient descent optimization is necessitated by the global approximation functions of neural networks. Fast learning rates are detrimental due to catastrophic interference, thus requiring the slow assimilation of experience.
2. Sparse Reward Problems: In environments where reward signals are infrequent, neural networks struggle to model the environment effectively. This class imbalance, skewed by low-reward occurrences, leads to an underestimation of high rewards, impacting the agent’s decision-making.
3. Reward Signal Propagation: Typical value-bootstrapping techniques propagate rewards one step at a time through the transition history. This inefficiency is exacerbated in environments with sparse rewards.

NEC tackles these inefficiencies by incorporating a semi-tabular representation for storing past experience, which offers slowly changing state representations alongside rapidly updated value function estimates. The critical innovation of NEC lies in its ability to immediately leverage high-reward strategies as they are encountered, without the delayed optimization typical of methods like DQN and A3C.

Methodology

The NEC architecture consists of a convolutional neural network for processing input states and a memory module, termed the Differentiable Neural Dictionary (DND), for storing key-value pairs associated with actions. Each action has a DND that offers fast, dictionary-like key-value access, enabling rapid updates of $Q$-values based on recent experiences.

A unique feature of NEC is the combination of Monte Carlo and off-policy $N$-step Q-learning estimates for updating the value function. This allows the agent to incorporate both on-policy reward information and backed-up off-policy estimates, offering a balanced and efficient learning process.

Empirical Results

The empirical validation on the Atari Learning Environment, a standard benchmark for DRL, demonstrated NEC's superiority in terms of data efficiency. NEC exhibited accelerated learning, achieving comparable or superior performance to established methods like Prioritized Replay and Retrace($\lambda$) with significantly fewer interactions. A detailed evaluation highlighted that within the first 5 million environment frames, NEC outperformed these models, showcasing an impressive ability for rapid learning.

Discussion and Implications

The implications of NEC are twofold—practical and theoretical. Practically, NEC provides a framework for applications requiring efficient learning from limited interactions, such as robotics and real-world decision making tasks. Theoretically, the integration of episodic memory into reinforcement learning frameworks offers insights into cognitive processes and how biological systems could inspire artificial intelligence models.

Future directions could explore the extension of NEC to more complex and diverse environments, potentially integrating it with hierarchical or multi-task learning frameworks to further enhance its applicability and robustness.

In sum, the introduction of NEC marks a forward step in addressing some of the pressing challenges in DRL, redefining the potential and scope of AI agents in environments necessitating quick adaptation and learning efficiency. This work lays a foundation for future exploration of non-parametric methods in reinforcement learning, advocating for the continued exploration of combining classic reinforcement mechanisms with modern neural network architectures.