Essays about: "Sparse rewards environment"
Found 5 essays containing the words Sparse rewards environment.
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1. The effects of multistep learning in the hard-exploration problem
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Reinforcement learning is a machine learning field which has received revitalised interest in later years due to many success stories and advancements in deep reinforcement learning. A key part in reinforcement learning is the need for exploration of the environment so the agent can properly learn the best policy. READ MORE
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2. Benchmarking Deep Reinforcement Learning on Continuous Control Tasks : AComparison of Neural Network Architectures and Environment Designs
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Deep Reinforcement Learning (RL) has received much attention in recent years. This thesis investigates how reward functions, environment termination conditions, Neural Network (NN) architectures, and the type of the deep RL algorithm aect the performance for continuous control tasks. READ MORE
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3. Asynchronous Advantage Actor-Critic and Flappy Bird
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Games provide ideal environments for assessingreinforcement learning algorithms because of their simple dynamicsand their inexpensive testing, compared to real-worldenvironments. Asynchronous Advantage Actor-Critic (A3C), developedby DeepMind, has shown significant improvements inperformance over other state-of-the-art algorithms on Atarigames. READ MORE
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4. An Evaluation of the Unity Machine Learning Agents Toolkit in Dense and Sparse Reward Video Game Environments
University essay from Uppsala universitet/Institutionen för speldesignAbstract : In computer games, one use case for artificial intelligence is used to create interesting problems for the player. To do this new techniques such as reinforcement learning allows game developers to create artificial intelligence agents with human-like or superhuman abilities. READ MORE
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5. Impact of observation noise and reward sparseness on Deep Deterministic Policy Gradient when applied to inverted pendulum stabilization
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Deep Reinforcement Learning (RL) algorithms have been shown to solve complex problems. Deep Deterministic Policy Gradient (DDPG) is a state-of-the-art deep RL algorithm able to handle environments with continuous action spaces. READ MORE
