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Deep reinforcement learning has also been applied to many domains beyond games. In robotics, it has been used to let robots perform simple household tasks [18] and solve a Rubik's cube with a robot hand. [19] [20] Deep RL has also found sustainability applications, used to reduce energy consumption at data centers. [21]
Model-free RL algorithms can start from a blank policy candidate and achieve superhuman performance in many complex tasks, including Atari games, StarCraft and Go.Deep neural networks are responsible for recent artificial intelligence breakthroughs, and they can be combined with RL to create superhuman agents such as Google DeepMind's AlphaGo.
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal.
In classical RL-based training of such bots, the reward function is simply correlated to how well the agent is performing in the game, usually using metrics like the in-game score. In comparison, in RLHF, a human is periodically presented with two clips of the agent's behavior in the game and must decide which one looks better.
Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent's decision function to accomplish difficult tasks. PPO was developed by John Schulman in 2017, [1] and had become the default RL algorithm at the US artificial intelligence company OpenAI. [2]
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods , and perform updates based on current estimates, like dynamic programming methods.
MuZero (MZ) is a combination of the high-performance planning of the AlphaZero (AZ) algorithm with approaches to model-free reinforcement learning. The combination allows for more efficient training in classical planning regimes, such as Go, while also handling domains with much more complex inputs at each stage, such as visual video games.
The mountain car problem. Mountain Car, a standard testing domain in Reinforcement learning, is a problem in which an under-powered car must drive up a steep hill.Since gravity is stronger than the car's engine, even at full throttle, the car cannot simply accelerate up the steep slope.
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