Temporal Difference Models: Model-Free Deep RL for Model-Based Control (1802.09081v2)

Abstract: Model-free reinforcement learning (RL) is a powerful, general tool for learning complex behaviors. However, its sample efficiency is often impractically large for solving challenging real-world problems, even with off-policy algorithms such as Q-learning. A limiting factor in classic model-free RL is that the learning signal consists only of scalar rewards, ignoring much of the rich information contained in state transition tuples. Model-based RL uses this information, by training a predictive model, but often does not achieve the same asymptotic performance as model-free RL due to model bias. We introduce temporal difference models (TDMs), a family of goal-conditioned value functions that can be trained with model-free learning and used for model-based control. TDMs combine the benefits of model-free and model-based RL: they leverage the rich information in state transitions to learn very efficiently, while still attaining asymptotic performance that exceeds that of direct model-based RL methods. Our experimental results show that, on a range of continuous control tasks, TDMs provide a substantial improvement in efficiency compared to state-of-the-art model-based and model-free methods.

An Analysis of Temporal Difference Models for Reinforcement Learning

Temporal Difference Models (TDMs) present an innovative approach within the reinforcement learning (RL) framework, designed to blend the efficiency of model-based methods with the asymptotic performance of model-free techniques. This paper discusses the development and implementation of TDMs, focusing on their ability to utilize goal-conditioned value functions for reinforcing model-based control. The central motivation is to harness the extensive information inherent in state transition dynamics, significantly enhancing the sample efficiency of RL tasks while maintaining competitive performance levels with traditional model-free approaches.

Theoretical Insights and Methodology

The foundation of TDMs lies in the idea of learning variable-horizon goal-conditioned value functions, which address both immediate and long-term prediction tasks. This allows TDMs to double as implicit dynamics models, providing a direct correlation with model-based RL. A pivotal insight is that model-free RL, typically limited to scalar reward signals, can gain substantial improvements by incorporating rich information from state transitions.

Key Concepts:

• Model-Free vs. Model-Based RL: Classic model-free approaches, while effective in terms of asymptotic performance, require significant sample inputs, as they exclusively rely on scalar rewards. In contrast, model-based RL methods employ extensive supervision to learn system dynamics, often leading to issues of bias and suboptimal policy derivation on complex tasks.
• Goal-Conditioned Value Functions: Goal-conditioned value functions are leveraged to predict rewards based on the feasibility of reaching particular goal states. TDMs enhance this concept by conditioning these value functions on a planning horizon, introducing dynamic goal attainment modeling w.r.t. various prediction timeframes.

Numerical Results

The authors present empirical evaluations across several continuous control tasks, including reaching, pushing, and locomotion. Results demonstrate that TDMs generally surpass traditional model-free and model-based techniques in terms of sample efficiency, achieving notable gains particularly in complex and large-dimensional tasks like the "Ant" environment. Two key factors contribute to this success:

• Efficient Use of Temporal Horizons: Variable horizons enable retrieval of valuable prediction insights at both short and long timeframes, facilitating effective planning that edges closer to real-world applicability.
• Vector-Valued Reward Structures: By expanding the typical scalar reward functions to vector-valued structures, TDMs maximize the quantity of information extracted from each interaction tuple, optimizing learning trajectories.

Future Directions

The paper offers numerous avenues for further exploration. Future developments could delve into more sophisticated planning algorithms that integrate seamlessly with TDM frameworks. Additionally, extensions involving high-dimensional, non-symbolic inputs (e.g., visual data) hold significant promise for real-world applications. Other considerations include enhancing the robustness of learned models against stochastic environmental factors, potentially involving adaptive techniques to offset variations and uncertainties in dynamics prediction.

In summary, Temporal Difference Models exemplify a significant step towards bridging the gap between model-free and model-based reinforcement learning. They provide a structured methodology capable of improving efficacy in scenarios demanding intelligent, autonomous control. As the computational paradigms in AI continue to evolve, such hybrid techniques are poised to redefine the boundaries of feasible RL applications.