Towards General-Purpose Model-Free Reinforcement Learning
Reinforcement learning (RL) promises a framework for near-universal
problem-solving. In practice however, RL algorithms are often tailored to
specific benchmarks, relying on carefully tuned hyperparameters and algorithmic
choices. Recently, powerful model-based RL methods have shown impressive
general results across benchmarks but come at the cost of increased complexity
and slow run times, limiting their broader applicability. In this paper, we
attempt to find a unifying model-free deep RL algorithm that can address a
diverse class of domains and problem settings. To achieve this, we leverage
model-based representations that approximately linearize the value function,
taking advantage of the denser task objectives used by model-based RL while
avoiding the costs associated with planning or simulated trajectories. We
evaluate our algorithm, MR.Q, on a variety of common RL benchmarks with a
single set of hyperparameters and show a competitive performance against
domain-specific and general baselines, providing a concrete step towards
building general-purpose model-free deep RL algorithms.
67986cbc7dbf69e4e38539b7
Scott Fujimoto
67986cbc7dbf69e4e38539b8
Pierluca D'Oro
admin_assigned
2025-01-28T14:09:37.496Z
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/avatars/82ca21cb9c8bacde071769bf4a888375.svg
Pierluca D'Oro
user
pierluca
67986cbc7dbf69e4e38539b9
Amy Zhang
67986cbc7dbf69e4e38539ba
Yuandong Tian
admin_assigned
2025-01-28T14:10:10.676Z
6344cf73ee1504dbcd5bdfe7
/avatars/6dd2bf1f9c5679e5c8c85d62c9836aac.svg
Yuandong Tian
user
tydsh
67986cbc7dbf69e4e38539bb
Michael Rabbat
Towards General-Purpose Model-Free Reinforcement Learning
Reinforcement learning (RL) promises a framework for near-universal
problem-solving. In practice however, RL algorithms are often tailored to
specific benchmarks, relying on carefully tuned hyperparameters and algorithmic
choices. Recently, powerful model-based RL methods have shown impressive
general results across benchmarks but come at the cost of increased complexity
and slow run times, limiting their broader applicability. In this paper, we
attempt to find a unifying model-free deep RL algorithm that can address a
diverse class of domains and problem settings. To achieve this, we leverage
model-based representations that approximately linearize the value function,
taking advantage of the denser task objectives used by model-based RL while
avoiding the costs associated with planning or simulated trajectories. We
evaluate our algorithm, MR.Q, on a variety of common RL benchmarks with a
single set of hyperparameters and show a competitive performance against
domain-specific and general baselines, providing a concrete step towards
building general-purpose model-free deep RL algorithms.
The paper presents MR.Q, a model-free deep RL algorithm that uses model-based representations to improve performance across diverse benchmarks without incurring high computational costs.
reinforcement learning model-based RL value function task objectives model-free RL MR.Q