# Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning

national conference on artificial intelligence, 2020.

Keywords:

Markov decision processhorizon methodgeneralized value functionshorizon returnDeep FHQ-learningMore(12+)

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Abstract:

We explore fixed-horizon temporal difference (TD) methods, reinforcement learning algorithms for a new kind of value function that predicts the sum of rewards over a $\textit{fixed}$ number of future time steps. To learn the value function for horizon $h$, these algorithms bootstrap from the value function for horizon $h-1$, or some sho...More

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Introduction

- Temporal difference (TD) methods (Sutton 1988) are an important approach to reinforcement learning (RL) that combine ideas from Monte Carlo estimation and dynamic programming.
- The learned values represent answers to questions about how a signal will accumulate over time, conditioned on a way of behaving
- In control tasks, this signal is the reward sequence, and the values represent an arbitrarily long sum of rewards an agent expects to receive when acting greedily with respect to its current predictions.
- Fixed-horizon agents can approximate infinite-horizon returns arbitrarily well, expand their set of learned horizons freely, and combine forecasts from multiple horizons to make time-sensitive predictions about rewards.

Highlights

- Temporal difference (TD) methods (Sutton 1988) are an important approach to reinforcement learning (RL) that combine ideas from Monte Carlo estimation and dynamic programming
- The RL problem is usually modeled as a Markov decision process (MDP), in which an agent interacts with an environment over a sequence of discrete time steps
- At each time step t, the agent receives information about the environment’s current state, St ∈ S, where S is the set of all possible states in the MDP
- We explore the convergence of fixed-horizon TD (FHTD) formally in Section 4
- We investigated using fixed-horizon returns in place of the conventional infinite-horizon return
- We argued that FHTD agents are stable under function approximation and have additional predictive power

Results

- For h = 1, ..., H, the following ODE system has an equilibrium.
- Wh+1,: = E (r(x, a, y) + wh,:φhy − wh+1,:φx)φTx (27).
- Finding an equilibrium point amounts to solving the following equations for all h: wh+1,:E[φxφTx ] = E[(r(x, a, y) + wh,:φhy )φTx ].
- Since the authors assume that the features are linearly independent, and using the fact that w0,: = 0, the authors can recursively solve these equations to find an equilibrium.
- Let the authors be the equilibrium point generated.
- Define w := w − the authors and substitute into Equation (27) to obtain the following system

Conclusion

**Discussion and future work**

In this work, the authors investigated using fixed-horizon returns in place of the conventional infinite-horizon return.- The authors derived FHTD methods and compared them to their infinite-horizon counterparts in terms of prediction capability, complexity, and performance.
- The authors proved convergence of FHTD methods with linear and general function approximation.
- In a tabular control problem, the authors showed that greedifying with respect to estimates of a short, fixed horizon could outperform doing so with respect to longer horizons.
- The authors demonstrated that FHTD methods can scale well to and perform competitively on a deep reinforcement learning control problem

Summary

## Introduction:

Temporal difference (TD) methods (Sutton 1988) are an important approach to reinforcement learning (RL) that combine ideas from Monte Carlo estimation and dynamic programming.- The learned values represent answers to questions about how a signal will accumulate over time, conditioned on a way of behaving
- In control tasks, this signal is the reward sequence, and the values represent an arbitrarily long sum of rewards an agent expects to receive when acting greedily with respect to its current predictions.
- Fixed-horizon agents can approximate infinite-horizon returns arbitrarily well, expand their set of learned horizons freely, and combine forecasts from multiple horizons to make time-sensitive predictions about rewards.
## Results:

For h = 1, ..., H, the following ODE system has an equilibrium.- Wh+1,: = E (r(x, a, y) + wh,:φhy − wh+1,:φx)φTx (27).
- Finding an equilibrium point amounts to solving the following equations for all h: wh+1,:E[φxφTx ] = E[(r(x, a, y) + wh,:φhy )φTx ].
- Since the authors assume that the features are linearly independent, and using the fact that w0,: = 0, the authors can recursively solve these equations to find an equilibrium.
- Let the authors be the equilibrium point generated.
- Define w := w − the authors and substitute into Equation (27) to obtain the following system
## Conclusion:

**Discussion and future work**

In this work, the authors investigated using fixed-horizon returns in place of the conventional infinite-horizon return.- The authors derived FHTD methods and compared them to their infinite-horizon counterparts in terms of prediction capability, complexity, and performance.
- The authors proved convergence of FHTD methods with linear and general function approximation.
- In a tabular control problem, the authors showed that greedifying with respect to estimates of a short, fixed horizon could outperform doing so with respect to longer horizons.
- The authors demonstrated that FHTD methods can scale well to and perform competitively on a deep reinforcement learning control problem

Funding

- We gratefully acknowledge funding from Alberta Innovates – Technology Futures, Google Deepmind, and from the Natural Sciences and Engineering Research Council of Canada

Reference

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- We assume throughout a common probability space (Ω, P, Σ). Our proof follows the general outline in Melo and Ribeiro (2007).
- Priouret (1990), we must construct another Markov chain so that H(wt, xt) in Benveniste, Metivier, and Priouret, p.213 (1990) has access to the TD error at time t. In the interests of completeness, we provide the full details below, but the reader may safely skip to the next section.
- We employ a variation of a standard approach, as in for example Tsitsiklis and Van Roy (1999). Let us define a new process Mt = (Xt, At, Xt+1, At+1). The process Mt has state space M:= X × A × X × A and σ-algebra σ(F × 2A × F )2A, with kernel Π defined first on M × F × 2A × F
- Proof. From Meyn and Tweedie, p.389 (2012), a Markov chain is uniformly ergodic iff it is νm-small for some m. We will show that Mt is ηm-small for some measure ηm. Since (Xt, At) is uniformly ergodic, let m > 0 and νm a non-trivial measure on F such that for all (x, a) ∈ X × A, B ∈ σ(F × 2A), Pm((x, a), B) ≥ νm(B).
- Note that our Mt corresponds to the Xt in Benveniste, Metivier, and Priouret, p.213 (1990). With this construction finished, we will assume in the following that whenever we refer to X or the Markov chain Xt, we are actually referring to (X × A)2 or the Markov chain Mt respectively.

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