[Paper Review] Learning Guidance Rewards with Trajectory-space Smoothing

Tanmay Gangwani, Yuan Zhou+1 more|arXiv (Cornell University)|Oct 23, 2020

Reinforcement Learning in Robotics36 references4 citations

TL;DR

This paper proposes a method to learn dense guidance rewards via trajectory-space smoothing, enabling efficient temporal credit assignment in deep reinforcement learning with sparse or delayed environmental rewards. By uniformly redistributing trajectory returns to each state-action pair, the approach provides dense supervision without training auxiliary networks, significantly improving sample efficiency across single- and multi-agent RL algorithms.

ABSTRACT

Long-term temporal credit assignment is an important challenge in deep reinforcement learning (RL). It refers to the ability of the agent to attribute actions to consequences that may occur after a long time interval. Existing policy-gradient and Q-learning algorithms typically rely on dense environmental rewards that provide rich short-term supervision and help with credit assignment. However, they struggle to solve tasks with delays between an action and the corresponding rewarding feedback. To make credit assignment easier, recent works have proposed algorithms to learn dense "guidance" rewards that could be used in place of the sparse or delayed environmental rewards. This paper is in the same vein -- starting with a surrogate RL objective that involves smoothing in the trajectory-space, we arrive at a new algorithm for learning guidance rewards. We show that the guidance rewards have an intuitive interpretation, and can be obtained without training any additional neural networks. Due to the ease of integration, we use the guidance rewards in a few popular algorithms (Q-learning, Actor-Critic, Distributional-RL) and present results in single-agent and multi-agent tasks that elucidate the benefit of our approach when the environmental rewards are sparse or delayed.

Motivation & Objective

• Address the challenge of long-term temporal credit assignment in deep reinforcement learning when environmental rewards are sparse or delayed.
• Develop a method that integrates seamlessly with existing RL algorithms to improve sample efficiency.
• Provide dense supervision through guidance rewards that are easily computable from trajectory returns.
• Ensure invariance to reward delay without requiring auxiliary neural networks or complex reward modeling.
• Enable effective credit assignment by redistributing the total return uniformly across all state-action pairs in a trajectory.

Proposed method

• Formulate a surrogate RL objective involving smoothing in the trajectory space to derive guidance rewards.
• Define guidance rewards as the expected return of past trajectories containing each state-action pair, enabling uniform credit assignment.
• Use only the trajectory return and transition dynamics to compute guidance rewards, avoiding training additional neural networks.
• Integrate guidance rewards into standard RL algorithms such as Q-learning, SAC, TD3, and Distributional-RL.
• Ensure the method remains equivalent to standard RL under the guidance reward function, preserving convergence properties.
• Apply trajectory-space smoothing to stabilize value estimation and improve credit assignment in delayed-reward environments.

Experimental results

Research questions

• RQ1 Can trajectory-space smoothing produce effective guidance rewards that improve credit assignment in delayed-reward RL?
• RQ2 How does the proposed method compare to existing approaches that use learned reward models or auxiliary networks?
• RQ3 To what extent can guidance rewards accelerate learning in environments with sparse or delayed environmental rewards?
• RQ4 Does the method maintain compatibility with a variety of standard RL algorithms, including off-policy and distributional methods?
• RQ5 Can the guidance rewards be computed efficiently without training additional models, while still enabling sample-efficient learning?

Key findings

• The guidance rewards provide dense supervision by uniformly redistributing the total trajectory return across all state-action pairs, enabling better value estimation.
• The method significantly improves learning speed and sample efficiency in MuJoCo locomotion tasks with delayed rewards, outperforming standard SAC and Q-learning.
• Integration with SAC, TD3, and Distributional-RL leads to faster convergence on both single-agent and multi-agent tasks with sparse or delayed rewards.
• The approach achieves performance comparable to or better than prior methods that use learned reward models, but without training additional neural networks.
• Experiments show that the guidance rewards are robust to reward delays and maintain stable learning across diverse environments.
• The method is easily plug-and-play, requiring no architectural changes to existing RL algorithms, and is compatible with off-policy and on-policy frameworks.