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Poster
Multi-step Greedy Reinforcement Learning Algorithms
Manan Tomar · Yonathan Efroni · Mohammad Ghavamzadeh

Tue Jul 14 12:00 PM -- 12:45 PM & Wed Jul 15 01:00 AM -- 01:45 AM (PDT) @
in Poster Session 6 »
Multi-step greedy policies have been extensively used in model-based reinforcement learning (RL), both when a model of the environment is available (e.g.,~in the game of Go) and when it is learned. In this paper, we explore their benefits in model-free RL, when employed using multi-step dynamic programming algorithms: $\kappa$-Policy Iteration ($\kappa$-PI) and $\kappa$-Value Iteration ($\kappa$-VI). These methods iteratively compute the next policy ($\kappa$-PI) and value function ($\kappa$-VI) by solving a surrogate decision problem with a shaped reward and a smaller discount factor. We derive model-free RL algorithms based on $\kappa$-PI and $\kappa$-VI in which the surrogate problem can be solved by any discrete or continuous action RL method, such as DQN and TRPO. We identify the importance of a hyper-parameter that controls the extent to which the surrogate problem is solved and suggest a way to set this parameter. When evaluated on a range of Atari and MuJoCo benchmark tasks, our results indicate that for the right range of $\kappa$, our algorithms outperform DQN and TRPO. This shows that our multi-step greedy algorithms are general enough to be applied over any existing RL algorithm and can significantly improve its performance.
Keywords: Deep Reinforcement Learning · Reinforcement Learning · Reinforcement Learning - General

Author Information

Manan Tomar (Facebook AI Research)
Yonathan Efroni (Technion)
Mohammad Ghavamzadeh (Google Research)

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