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- 1Artificial Intelligence
- 1Atari 2600
- 1Exploration
- 1Games
- 1Goal-Conditioned Reinforcement Learning
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Spring 2010
This research focuses on developing AI agents that play arbitrary Atari 2600 console games without having any game-specific assumptions or prior knowledge. Two main approaches are considered: reinforcement learning based methods and search based methods. The RL-based methods use feature vectors...
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Improving Deep Deterministic Policy Gradient for Sparse Reward and Goal-Conditioned Continuous Control
DownloadSpring 2024
We propose an improved version of deep deterministic policy gradient (DDPG) for sparse reward and goal-conditioned reinforcement learning. To enhance exploration, we introduce \emph{${\epsilon}{t}$-greedy}, which uses search to generate exploratory options, focusing on less-visited states. We...