000			02033nam a22002417a 4500
003			OSt
005			20260428124857.0
008			260428b |||||||| |||| 00| 0 eng d
020			_a9781839210686
041			_aeng
082			_a006.31 RAV-D
100			_aRavichandiran, Sudharsan _976796
245			_aDeep Reinforcement Learning with Python _bMaster classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow
250			_a2nd.
260			_aMumbai _bPackt Publishing _c2020
300			_axxi, 730p.
520			_aWith significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.
650			_aArtificial intelligence _976797
650			_aMachine learning _976798
650			_aReinforcement learning (RL) _976799
942			_cBK
999			_c199938 _d199938