Deep reinforcement learning uses simulators as abstract oracles to interact with the environment. In continuous domains of multi body robotic systems, differentiable simulators have recently been proposed but are yet under utilized, even though we have the knowledge to make them produce richer information. This problem when juxtaposed with the usually high computational cost of exploration-exploitation in high dimensional state space can quickly render reinforcement learning algorithms impractical. In this paper, we propose to combine learning and simulators such that the quality of both increases, while the need to exhaustively search the state space decreases. We propose to learn value function and state, control trajectories through the ...
While operational space control is of essential importance for robotics and well-understood from an ...
While operational space control is of essential importance for robotics and well-understood from an ...
Reinforcement learning (RL) and trajectory optimization (TO) present strong complementary advantages...
Deep reinforcement learning uses simulators as abstract oracles to interact with the environment. In...
Deep reinforcement learning uses simulators as abstract oracles to interact with the environment. In...
Deep reinforcement learning uses simulators as abstract oracles to interact with the environment. In...
International audienceThe recent successes in deep reinforcement learning largely rely on the capabi...
International audienceThe recent successes in deep reinforcement learning largely rely on the capabi...
Recent advances in machine learning, simulation, algorithm design, and computer hardware have allowe...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2...
Recent advances in machine learning, simulation, algorithm design, and computer hardware have allowe...
We consider the problem of optimization in policy space for reinforcement learning. While a plethora...
Reinforcement learning is a powerful approach for learning control policies that solve sequential de...
Reinforcement learning (RL) and trajectory optimization (TO) present strong complementary advantages...
Reinforcement learning (RL) and trajectory optimization (TO) present strong complementary advantages...
While operational space control is of essential importance for robotics and well-understood from an ...
While operational space control is of essential importance for robotics and well-understood from an ...
Reinforcement learning (RL) and trajectory optimization (TO) present strong complementary advantages...
Deep reinforcement learning uses simulators as abstract oracles to interact with the environment. In...
Deep reinforcement learning uses simulators as abstract oracles to interact with the environment. In...
Deep reinforcement learning uses simulators as abstract oracles to interact with the environment. In...
International audienceThe recent successes in deep reinforcement learning largely rely on the capabi...
International audienceThe recent successes in deep reinforcement learning largely rely on the capabi...
Recent advances in machine learning, simulation, algorithm design, and computer hardware have allowe...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2...
Recent advances in machine learning, simulation, algorithm design, and computer hardware have allowe...
We consider the problem of optimization in policy space for reinforcement learning. While a plethora...
Reinforcement learning is a powerful approach for learning control policies that solve sequential de...
Reinforcement learning (RL) and trajectory optimization (TO) present strong complementary advantages...
Reinforcement learning (RL) and trajectory optimization (TO) present strong complementary advantages...
While operational space control is of essential importance for robotics and well-understood from an ...
While operational space control is of essential importance for robotics and well-understood from an ...
Reinforcement learning (RL) and trajectory optimization (TO) present strong complementary advantages...