Planning to Explore via Self-Supervised World Models (2005.05960v2)

Abstract: Reinforcement learning allows solving complex tasks, however, the learning tends to be task-specific and the sample efficiency remains a challenge. We present Plan2Explore, a self-supervised reinforcement learning agent that tackles both these challenges through a new approach to self-supervised exploration and fast adaptation to new tasks, which need not be known during exploration. During exploration, unlike prior methods which retrospectively compute the novelty of observations after the agent has already reached them, our agent acts efficiently by leveraging planning to seek out expected future novelty. After exploration, the agent quickly adapts to multiple downstream tasks in a zero or a few-shot manner. We evaluate on challenging control tasks from high-dimensional image inputs. Without any training supervision or task-specific interaction, Plan2Explore outperforms prior self-supervised exploration methods, and in fact, almost matches the performances oracle which has access to rewards. Videos and code at https://ramanans1.github.io/plan2explore/

• The paper introduces Plan2Explore, which uses intrinsic rewards from latent disagreement to proactively guide exploration.
• The paper details a task-agnostic framework that gathers diverse data, enabling rapid adaptation via zero- or few-shot learning.
• The paper demonstrates state-of-the-art performance on continuous control tasks, nearly matching oracle agents with substantially fewer interactions.

Overview of "Planning to Explore via Self-Supervised World Models"

The paper "Planning to Explore via Self-Supervised World Models," authored by researchers from institutions including the University of Pennsylvania and UC Berkeley, introduces Plan2Explore, an advanced reinforcement learning (RL) agent designed to address the challenges of task-specific learning and sample efficiency in high-dimensional environments. Plan2Explore utilizes self-supervised learning principles to explore environments effectively and adapt to new tasks with minimal additional data.

Core Concepts and Methodology

Plan2Explore is predicated on the necessity of intrinsic motivation for efficient exploration in high-dimensional spaces such as images, where reward-based traditional learning is inefficient or impractical. The agent diverges from prior methods by proactively planning to encounter novel states rather than retrospectively evaluating novelty after state visitation, thereby improving exploration efficiency.

1. Task-Agnostic RL: Plan2Explore collects diverse datasets through exploration without reward signals, facilitating task-agnostic data accumulation. Once downstream tasks are introduced via reward functions, the agent rapidly adapts using zero or few-shot learning approaches.
2. Intrinsic Motivation through Latent Disagreement: The agent utilizes intrinsic rewards derived from "latent disagreement," a concept where variance across predictive models serves as an uncertainty measure. An ensemble of models predicts the next state, and the divergence in predictions quantifies novelty, providing an internal motivation for the agent to explore less familiar regions of the state space.
3. Planning to Explore: Leveraging a learned world model, Plan2Explore anticipates future state novelties by simulating scenarios within a compact latent space. This proactive approach allows the policy to be optimized using imagined trajectories, reducing the need for environment interactions and enhancing data efficiency.

Numerical Results and Performance Metrics

The paper demonstrates that Plan2Explore achieves state-of-the-art performance on a variety of continuous control tasks, using DM Control Suite benchmarks as a testbed. It shows that Plan2Explore can nearly match the performance of oracle agents that have access to task-specific rewards, despite operating in a self-supervised setting. The numerical superiority is evident in both zero-shot and few-shot performance across multiple tasks, indicating robust exploration capabilities.

The results are benchmarked against baseline methods such as Dreamer, Curiosity Driven Exploration, and Model-based Active eXploration (MAX), showcasing Plan2Explore's efficacy without extrinsic rewards. Notably, in the few-shot learning paradigm, Plan2Explore converges to task-specific solutions with significantly fewer interactions compared to traditional methods.

Theoretical and Practical Implications

Theoretically, the paper positions its exploration framework within the domain of information gain, wherein the intrinsic reward mechanism effectively approximates the expected information gain through model disagreement. This novel insight bridges concepts from active learning and Bayesian exploration into model-based reinforcement learning, providing a robust framework for scalable exploration.

Practically, Plan2Explore's ability to utilize latent dynamics reduces computational overhead and scales effectively with high-dimensional inputs like images. Its architecture allows it to generalize across tasks, exhibiting potential applicability in domains requiring adaptive learning without extensive task-specific data, such as robotics and autonomous systems.

Future Directions

This research opens several avenues for future exploration. The understanding and enhancement of intrinsic motivation mechanisms will likely play a pivotal role in developing more generalized RL agents. Further research might explore scaling Plan2Explore to more complex, real-world tasks, incorporating multi-agent systems, and extending the architecture to multi-task learning environments.

In summary, "Planning to Explore via Self-Supervised World Models" makes significant contributions to the field of model-based reinforcement learning through its innovative use of self-supervised exploration strategies, expanding the understanding of how agents can efficiently learn and adapt to new tasks without specific reward signals.