computer-vision-11
Reinforcement Learning (RL)
3260 papers • 126 benchmarks • 313 datasets
Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.
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Continuous control with deep reinforcement learning
A. Pritzel, N. Heess, Daan Wierstra, David Silver, T. Lillicrap, Jonathan J. Hunt, Tom Erez, Yuval Tassa•Tue Sep 08 2015
This work presents an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces, and demonstrates that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.
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