TLDR: Unsupervised Goal-Conditioned RL via Temporal Distance-Aware Representations
(2407.08464)Abstract
Unsupervised goal-conditioned reinforcement learning (GCRL) is a promising paradigm for developing diverse robotic skills without external supervision. However, existing unsupervised GCRL methods often struggle to cover a wide range of states in complex environments due to their limited exploration and sparse or noisy rewards for GCRL. To overcome these challenges, we propose a novel unsupervised GCRL method that leverages TemporaL Distance-aware Representations (TLDR). TLDR selects faraway goals to initiate exploration and computes intrinsic exploration rewards and goal-reaching rewards, based on temporal distance. Specifically, our exploration policy seeks states with large temporal distances (i.e. covering a large state space), while the goal-conditioned policy learns to minimize the temporal distance to the goal (i.e. reaching the goal). Our experimental results in six simulated robotic locomotion environments demonstrate that our method significantly outperforms previous unsupervised GCRL methods in achieving a wide variety of states.
Overview
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The paper introduces TLDR, a novel method for unsupervised goal-conditioned reinforcement learning (GCRL) that uses temporal distance-aware representations to enhance exploration and policy learning.
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TLDR leverages temporal distances between states to select exploratory goals, provide intrinsic exploration rewards, and train goal-conditioned policies, significantly improving state coverage and goal-reaching performance in complex environments.
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Extensive experiments and ablation studies demonstrate TLDR's effectiveness and potential future directions include addressing temporal asymmetry, integrating model-based approaches, and optimizing training efficiency in pixel-based environments.
Unsupervised Goal-Conditioned RL via Temporal Distance-Aware Representations
The paper introduces a novel unsupervised goal-conditioned reinforcement learning (GCRL) method, referred to as TemporaL Distance-aware Representations (TLDR). This approach aims to address significant challenges in existing unsupervised GCRL methods, which often struggle with limited exploration and sparse or noisy rewards, particularly in complex environments. The TLDR method seeks to enhance exploration and goal-conditioned policy learning by leveraging temporal distances between states.
Methodology
The principal innovation of the TLDR method lies in the use of temporal distance-aware representations. These representations are designed to capture the temporal distance between states, defined as the minimum number of environment steps required to transition from one state to another. The TLDR method employs these representations for three critical objectives within the GCRL framework:
- Exploratory Goal Selection: TLDR selects exploratory goals that are temporally distant from already visited states. This is achieved using a non-parametric particle-based entropy estimator on the temporal distance representation space, encouraging the agent to explore a broader state space.
- Intrinsic Exploration Rewards: The exploration policy incentivizes visiting states that maximize the temporal distance from the agent's current trajectory, thereby promoting the discovery of novel and temporally far-reaching states.
- Goal-Conditioned Policy Learning: The goal-conditioned policy is trained to minimize the temporal distance to a given goal, providing a more dense and informative reward signal compared to traditional sparse rewards.
The TLDR algorithm integrates these components within the Go-Explore framework. Empirical temporal distances, learned through a state encoder network, are used to inform both the selection of exploratory goals and the computation of rewards. The loss function for learning temporal distance-aware representations incorporates a softplus function that weighs temporal distances and a constraint to ensure the representation preserves the temporal distance structure.
Experimental Evaluation
The effectiveness of TLDR is demonstrated through extensive experiments on six robotic locomotion environments and two pixel-based environments. The environments include Ant, HalfCheetah, Humanoid-Run, Quadruped-Escape, AntMaze-Large, AntMaze-Ultra, Quadruped (Pixel), and Kitchen (Pixel).
State Coverage
In state-based environments, the paper reports superior state coverage across diverse environments, outperforming several baselines such as METRA, PEG, APT, RND, and Disagreement. Notably, TLDR achieved higher state coverage in complex environments like AntMaze-Large and AntMaze-Ultra, where other methods showed limited exploration capabilities.
Goal-Reaching Performance
When evaluating goal-reaching performance, TLDR demonstrated competitive or superior performance in proximity to goals in environments such as Ant and HalfCheetah. In maze environments, TLDR significantly outperformed prior methods in reaching pre-defined goals, covering a larger set of objectives in both AntMaze-Large and AntMaze-Ultra.
Ablation Studies
The paper also includes insightful ablation studies. These studies evaluate the impact of different exploration strategies and reward designs on the performance of TLDR. The results underscored the advantage of using temporal distance-based strategies for both exploration and goal-conditioned policy learning, as compared to alternative methods like RND, APT, and Disagreement.
Implications and Future Directions
The TLDR method offers promising implications for enhancing the exploratory behavior and learning efficiency of unsupervised GCRL algorithms. The use of temporal distance-aware representations provides a task-agnostic metric that can be generalized across various environments, making the approach broadly applicable.
From a theoretical perspective, the temporal distance-aware representation mechanism enriches the intrinsic reward signals, facilitating the learning of long-horizon behaviors without external supervision. This could serve as a foundational technique for developing and pre-training intelligent agents capable of autonomously acquiring diverse and complex skills.
Looking ahead, several future directions can be envisioned:
- Temporal Asymmetry: Addressing the limitation of symmetric temporal distances in the representations. This enhancement could lead to better policy learning in environments where temporal transitions are inherently asymmetric.
- Model-Based Integration: Incorporating model-based approaches to further improve the sample efficiency of TLDR, leveraging the dense reward signals provided by temporal distances.
- Scalability and Efficiency: Exploring optimization strategies to improve training efficiency, especially in pixel-based environments where TLDR has shown relatively slower learning speeds.
In conclusion, the TLDR method represents a significant advancement in unsupervised GCRL, introducing a robust framework for leveraging temporal distances to enhance exploration and policy learning. This method sets the stage for future innovations in autonomous skill discovery and the development of versatile robotic systems.