Reinforcement Learning
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method
Based on our regularization, we propose an off-policy actor-critic algorithm.
MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning Agents
The high variance stems from the lack of structural credit assignment, i.e. a single scalar
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
In other words, the assumptions in these works imply that the state space is already well-explored.
On the Importance of Exploration for Generalization in Reinforcement Learning
We hypothesize that the agent's exploration strategy plays a key role in its ability to generalize to new environments.
Sequential Causal Imitation Learning with Unobserved Confounders
"Monkey see monkey do" is an age-old adage, referring to naรฏve imitation without a
What Matters for Adversarial Imitation Learning?
Adversarial imitation learning has become a popular framework for imitation in continuous control.
A Omitted Proofs
The proofs of these propositions are extended from Berlekamp (1968). Note that both oracle's preference feedback and We adopt the environment setting created by Rothfuss et al. (2019). MuJoCo locomotion tasks, where the reward function are varied to create a multi-task setting. The training and testing tasks are randomly generated by a fixed random seed. During meta-training, the meta-RL algorithm has the full access to the environmental interaction.