Original: We design the following experimental procedure to demonstrate this ability: (1) train the model on dataset D 1 which contains two contexts ( C 0 and C 1 ); (2) reset free parameters in the variational distribution q ( ν | ˆ ν ) and q ( µ | ˆ µ ) while preserving q ( θ | ˆ θ ) ; (3) re-train the model on dataset D 2 which contains the two original contexts and two new contexts ( C 2 and
Revised: We design the following experimental procedure to demonstrate this ability: (1) train the model on dataset D 1 which contains two contexts ( C 0 and C 1 ); (2) reset free parameters in the variational distribution q ( ν | ˆ ν ) and q ( µ | ˆ µ ) while preserving q ( θ | ˆ θ ) ; (3) re-train the model on dataset D 2 which contains the two original contexts and two new contexts ( C 2 and

Original: C 3 ).
Revised: C 3 ).

Original: We present the training results on the sets D 1 and D 2 in Figure A16 and A17, respectively.
Revised: We present the training results on the sets D 1 and D 2 in Figure A16 and A17, respectively.

Original: a
Revised: a

Original: These results suggest that our model is able to learn new contexts ( C 2 and C 3 in Figure A17), while preserving the original contexts ( C 0 and C 1 in both Figures A16 and A17).
Revised: These results suggest that our model is able to learn new contexts ( C 2 and C 3 in Figure A17), while preserving the original contexts ( C 0 and C 1 in both Figures A16 and A17).

Original: Z
Revised: Z

Original: Z Z
Revised: Z Z

Original: Z Z
Revised: Z Z

Original: Z Z
Revised: Z Z

Original: Z Z
Revised: Z Z

Original: Zp ( z 0 ) 87.9 0.
Revised: Zp ( z 0 ) 87.9 0.

Original: P r o b a b ili t y ( % ) (a) Learned context model on D    80 Time ( t )
Revised: P r o b a b ili t y ( % ) (a) Learned context model on D    80 Time ( t )

Original: C C
Revised: C C

Original: Z Z
Revised: Z Z

Original: Z Z
Revised: Z Z

Original: Z(b)
Revised: Z(b)

Original: Learned context transitions on D
Revised: Learned context transitions on D

Original: [Figure]
Revised: [Figure]

Original: E.
Revised: E.

Original: C OMPARING TO POMDP AND C-MDP METHODS
Revised: C OMPARING TO POMDP AND C-MDP METHODS

Original: algo ↓ failure → hard soft α = 

Revised: algo ↓ failure → hard soft α =

Original: .soft α
Revised: .soft α

Original: = 

Revised: =

Original: .soft α = 0 .
Revised: .soft α = 0 .

Original: FI-SAC 84 .
Revised: FI-SAC 84 .

Original: 50 ± 1 .76 .
Revised: 50 ± 1 .76 .

Original: 63 ± 8 .84 .
Revised: 63 ± 8 .84 .

Original: 75 ± 3 .86 .
Revised: 75 ± 3 .86 .

Original: 92 ± 1 .
Revised: 92 ± 1 .

Original: C-SAC 85 .
Revised: C-SAC 85 .

Original: 38 ± 1 .76 .
Revised: 38 ± 1 .76 .

Original: 80 ± 8 .86 .
Revised: 80 ± 8 .86 .

Original: 76 ± 2 .88 .
Revised: 76 ± 2 .88 .

Original: 35 ± 1 .
Revised: 35 ± 1 .

Original: C-CEM 87 .
Revised: C-CEM 87 .

Original: 63 ± 0 .
Revised: 63 ± 0 .

Original: [Table]
Revised: [Table]

Original: a
Revised: a

Original: We chosen the context set as C = {− 1 , χ } for χ ∈ {− 1 , 0 . 1 , 0 . 3 , 0 . 5 } .
Revised: We chosen the context set as C = {− 1 , χ } for χ ∈ {− 1 , 0 . 1 , 0 . 3 , 0 . 5 } .

Original: However, for χ > 0 , we again increased the force magnitude by two-fold.
Revised: However, for χ > 0 , we again increased the force magnitude by two-fold.

Original: For SAC-based algorithms we present the statistics for 50 episodes and 3 seeds.
Revised: For SAC-based algorithms we present the statistics for 50 episodes and 3 seeds.

Original: To avoid unfair comparison to POMDP and continual RL methods, we pick their best performance and explain why these methods are not well-suited for our problem.
Revised: To avoid unfair comparison to POMDP and continual RL methods, we pick their best performance and explain why these methods are not well-suited for our problem.

Original: For
Revised: For

Original: GPMM we run ten separate experiments with 15 episode each and picked three best runs and the best episode performance.
Revised: GPMM we run ten separate experiments with 15 episode each and picked three best runs and the best episode performance.

Original: For RNN-PPO we run three experiments, but picked the best learned policy over time and over the runs.
Revised: For RNN-PPO we run three experiments, but picked the best learned policy over time and over the runs.

Original: We present our experimental results in Table A6.
Revised: We present our experimental results in Table A6.

Original: While it seems that
Revised: While it seems that

Original: RNN-PPO learns to swing-up correctly for soft failures and GPMM almost learns to swing-up for χ = 0 .
Revised: RNN-PPO learns to swing-up correctly for soft failures and GPMM almost learns to swing-up for χ = 0 .

Original: 5 , the learned belief models indicate that this not so.
Revised: 5 , the learned belief models indicate that this not so.

Original: In fact, plotting the evolution of the belief for RNN-PPO and the context evolution for GPMM illustrates that the algorithms do not learn the context model (see Figure 4 in the main text).
Revised: In fact, plotting the evolution of the belief for RNN-PPO and the context evolution for GPMM illustrates that the algorithms do not learn the context model (see Figure 4 in the main text).

Original: a
Revised: a