Training Agents using Upside-Down Reinforcement Learning (1912.02877v2)

Abstract: We develop Upside-Down Reinforcement Learning (UDRL), a method for learning to act using only supervised learning techniques. Unlike traditional algorithms, UDRL does not use reward prediction or search for an optimal policy. Instead, it trains agents to follow commands such as "obtain so much total reward in so much time." Many of its general principles are outlined in a companion report; the goal of this paper is to develop a practical learning algorithm and show that this conceptually simple perspective on agent training can produce a range of rewarding behaviors for multiple episodic environments. Experiments show that on some tasks UDRL's performance can be surprisingly competitive with, and even exceed that of some traditional baseline algorithms developed over decades of research. Based on these results, we suggest that alternative approaches to expected reward maximization have an important role to play in training useful autonomous agents.

• The paper presents a novel paradigm that reframes reinforcement learning as a supervised learning task, bypassing traditional reward prediction.
• It trains a behavior function by using past interaction data to map desired returns and horizons to optimal actions.
• Experimental results show competitive performance in sparse-reward and partially observable environments, matching or exceeding established algorithms.

Upside-Down Reinforcement Learning: An Expert Review

The paper "Training Agents using Upside-Down Reinforcement Learning" presents a novel methodology for training reinforcement learning (RL) agents by utilizing a supervised learning (SL) framework. The authors introduce Upside-Down Reinforcement Learning (UDRL), a paradigm that distinguishes itself by eschewing the traditional reward prediction and optimal policy search strategies. Instead, UDRL proposes training agents to execute commands framed as desired outcomes, like achieving a particular reward within a defined time frame.

Core Contributions and Methodology

UDRL shifts the RL problem towards SL methodologies, where agents are trained through a process of interpreting past experiences as instances of command fulfiLLMent. The innovative aspect of UDRL lies in its behavioral modeling, where a behavior function replaces the traditional action-value function. This function inverts the relationship between actions and returns: it takes desired returns and horizons as inputs to predict optimal actions, in contrast to predicting returns for given actions.

The training of a behavior function is achieved by constructing a dataset from past episodes of interaction, where trajectories are segmented to derive inputs of the form $(s_t, d^r, d^h)$, representing states, desired returns, and horizons, respectively. Supervised learning techniques are then applied to optimize policy reproduction for these input conditions. This retrospective learning strategy allows UDRL to handle delayed rewards effectively and enables agents to generalize well to new situations.

Empirical Results

Experimental validation across various environments with both discrete and continuous action spaces demonstrates UDRL's competitive performance. Notably, UDRL shows advantages in scenarios with sparse rewards and partially observable states due to its ability to directly handle retrospective command labeling. Specifically, the approach matched or exceeded the performance of established algorithms like DQN, A2C, TRPO, PPO, and DDPG in several benchmark tasks.

Additionally, UDRL has demonstrated the ability to follow varied commands, such as adjusting its behavior in response to different desired return settings within the same trained model, showcasing flexibility that implicates broader generalization capabilities compared to traditional RL methods.

Theoretical and Practical Implications

The UDRL approach, by framing the RL task as an SL problem, opens new avenues for leveraging advances in SL methodologies within RL contexts. This conceptual shift introduces the potential for more robust and scalable training protocols by circumventing issues related to non-stationary targets typical in RL algorithms.

Practically, UDRL's methodology could benefit applications where specified objectives are dynamically adjusted, such as robotic control and operational planning tasks. The characteristically straightforward approach of UDRL also implies reduced computational overhead, favoring environments with minimal stochasticity.

Future Directions

The novelty of UDRL and its supportive experimental evidence suggest promising routes for extension. Future research could explore richer command structures, enhanced exploration strategies, and integration with traditional RL or model-based methods. Additionally, theoretical exploration into UDRL's performance bounds in stochastic environments would be beneficial for delineating its applicability and optimization opportunities.

In conclusion, UDRL introduces a compelling perspective to the RL landscape, challenging conventional boundaries and suggesting that SL's robustness and scalability can play a central role in evolving RL paradigms. However, balancing its theoretical foundations and practical performances remains an open challenge, necessitating further exploration.